Show of Hands:Votes: Party classification:: Votes_Age_Q109244_cnk02
bdanalytics
Date: (Wed) Jun 01, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret## Loading required package: lattice## Loading required package: ggplot2source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
# glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet") else
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Votes_Age_Q109244_cnk02_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "select.features" #default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL #default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/Votes_Age_Q109244_cnk01_partition.data.training.RData" # "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#load("Votes_Q_02_cnk_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 select.features 1 0 0 5.859 NA NAStep 1.0: select features
chunk option: eval=
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)} #{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx, keep = c(“glbFeatsCategory”,“glb_dsp_cols”))}
Step 1.0: select features
Step 1.0: select features
```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## Loading required package: reshape2## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7689"
## [1] "cor(Party.fctr, Q98059.fctr)=-0.0172"
## [1] "cor(Party.fctr, Q98078.fctr)=-0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr## [1] "cor(Q99480.fctr, Q99581.fctr)=0.7660"
## [1] "cor(Party.fctr, Q99480.fctr)=0.0344"
## [1] "cor(Party.fctr, Q99581.fctr)=0.0104"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99581.fctr as highly correlated with Q99480.fctr## [1] "cor(Q108855.fctr, Q108856.fctr)=0.7430"
## [1] "cor(Party.fctr, Q108855.fctr)=0.0371"
## [1] "cor(Party.fctr, Q108856.fctr)=0.0140"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108856.fctr as highly correlated with Q108855.fctr## [1] "cor(Q122770.fctr, Q122771.fctr)=0.7379"
## [1] "cor(Party.fctr, Q122770.fctr)=0.0195"
## [1] "cor(Party.fctr, Q122771.fctr)=0.0348"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q122770.fctr as highly correlated with Q122771.fctr## [1] "cor(Q106272.fctr, Q106388.fctr)=0.7339"
## [1] "cor(Party.fctr, Q106272.fctr)=0.0401"
## [1] "cor(Party.fctr, Q106388.fctr)=0.0342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q106388.fctr as highly correlated with Q106272.fctr## [1] "cor(Q100680.fctr, Q100689.fctr)=0.7292"
## [1] "cor(Party.fctr, Q100680.fctr)=-0.0158"
## [1] "cor(Party.fctr, Q100689.fctr)=-0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100689.fctr## [1] "cor(Q99480.fctr, Q99716.fctr)=0.7252"
## [1] "cor(Party.fctr, Q99480.fctr)=0.0344"
## [1] "cor(Party.fctr, Q99716.fctr)=-0.0209"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99716.fctr as highly correlated with Q99480.fctr## [1] "cor(Q120472.fctr, Q120650.fctr)=0.7126"
## [1] "cor(Party.fctr, Q120472.fctr)=0.0462"
## [1] "cor(Party.fctr, Q120650.fctr)=0.0271"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q120650.fctr as highly correlated with Q120472.fctr## [1] "cor(Q98869.fctr, Q99480.fctr)=0.7084"
## [1] "cor(Party.fctr, Q98869.fctr)=0.0277"
## [1] "cor(Party.fctr, Q99480.fctr)=0.0344"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98869.fctr as highly correlated with Q99480.fctr## [1] "cor(Q123464.fctr, Q123621.fctr)=0.7078"
## [1] "cor(Party.fctr, Q123464.fctr)=0.0136"
## [1] "cor(Party.fctr, Q123621.fctr)=0.0255"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q123464.fctr as highly correlated with Q123621.fctr## [1] "cor(Q108754.fctr, Q108855.fctr)=0.7005"
## [1] "cor(Party.fctr, Q108754.fctr)=0.0081"
## [1] "cor(Party.fctr, Q108855.fctr)=0.0371"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108754.fctr as highly correlated with Q108855.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## Gender.fctr 0.1027400851 0 0.1027400851 <NA>
## Q115611.fctr 0.0904468203 0 0.0904468203 <NA>
## Q113181.fctr 0.0808753072 0 0.0808753072 <NA>
## Q98197.fctr 0.0549342527 0 0.0549342527 <NA>
## Q120472.fctr 0.0462030674 0 0.0462030674 <NA>
## Q116881.fctr 0.0416860293 0 0.0416860293 <NA>
## Q101596.fctr 0.0409784077 0 0.0409784077 <NA>
## Q106272.fctr 0.0400926462 0 0.0400926462 <NA>
## Q110740.fctr 0.0380691243 0 0.0380691243 <NA>
## Q108855.fctr 0.0370970211 0 0.0370970211 <NA>
## Q122771.fctr 0.0348421015 0 0.0348421015 <NA>
## Q99480.fctr 0.0344412239 0 0.0344412239 <NA>
## Q106388.fctr 0.0341579350 0 0.0341579350 Q106272.fctr
## Q115899.fctr 0.0324177950 0 0.0324177950 <NA>
## Q120014.fctr 0.0318620439 0 0.0318620439 <NA>
## Q107869.fctr 0.0304661021 0 0.0304661021 <NA>
## USER_ID 0.0302304868 1 0.0302304868 <NA>
## .pos 0.0302037138 1 0.0302037138 <NA>
## Q98869.fctr 0.0276734114 0 0.0276734114 Q99480.fctr
## Q120650.fctr 0.0270889067 0 0.0270889067 Q120472.fctr
## Q122769.fctr 0.0259739146 0 0.0259739146 <NA>
## Q123621.fctr 0.0255329743 0 0.0255329743 <NA>
## Q118117.fctr 0.0253544150 0 0.0253544150 <NA>
## Q116441.fctr 0.0237358205 0 0.0237358205 <NA>
## Q122120.fctr 0.0229287700 0 0.0229287700 <NA>
## Q119334.fctr 0.0226894034 0 0.0226894034 <NA>
## Q106993.fctr 0.0207428635 0 0.0207428635 <NA>
## Q105655.fctr 0.0198994078 0 0.0198994078 <NA>
## Q117186.fctr 0.0198853672 0 0.0198853672 <NA>
## Q122770.fctr 0.0194639697 0 0.0194639697 Q122771.fctr
## Q114152.fctr 0.0175013163 0 0.0175013163 <NA>
## Q120194.fctr 0.0172986920 0 0.0172986920 <NA>
## Q118232.fctr 0.0171321152 0 0.0171321152 <NA>
## Income.fctr 0.0159635458 0 0.0159635458 <NA>
## Q116197.fctr 0.0158561766 0 0.0158561766 <NA>
## Q102289.fctr 0.0155850393 0 0.0155850393 <NA>
## Q118233.fctr 0.0147269325 0 0.0147269325 <NA>
## Q108856.fctr 0.0140363785 0 0.0140363785 Q108855.fctr
## Q99982.fctr 0.0139727928 0 0.0139727928 <NA>
## Q117193.fctr 0.0138241599 0 0.0138241599 <NA>
## Q123464.fctr 0.0136140083 0 0.0136140083 Q123621.fctr
## Q111580.fctr 0.0132382335 0 0.0132382335 <NA>
## Q119650.fctr 0.0125645475 0 0.0125645475 <NA>
## Q118237.fctr 0.0117079669 0 0.0117079669 <NA>
## YOB 0.0116828198 1 0.0116828198 <NA>
## Q112270.fctr 0.0116157798 0 0.0116157798 <NA>
## Q116797.fctr 0.0112749656 0 0.0112749656 <NA>
## Q124742.fctr 0.0111642906 0 0.0111642906 <NA>
## Q99581.fctr 0.0103662478 0 0.0103662478 Q99480.fctr
## Q115777.fctr 0.0101315203 0 0.0101315203 <NA>
## Q101162.fctr 0.0099412952 0 0.0099412952 <NA>
## Q98578.fctr 0.0081164509 0 0.0081164509 <NA>
## Q108754.fctr 0.0080847764 0 0.0080847764 Q108855.fctr
## .rnorm 0.0078039520 0 0.0078039520 <NA>
## Q106389.fctr 0.0077498918 0 0.0077498918 <NA>
## Q96024.fctr 0.0069116541 0 0.0069116541 <NA>
## Q108343.fctr 0.0060665340 0 0.0060665340 <NA>
## Q112512.fctr 0.0056768212 0 0.0056768212 <NA>
## Q120978.fctr 0.0044187616 0 0.0044187616 <NA>
## Q106997.fctr 0.0041749086 0 0.0041749086 <NA>
## YOB.Age.dff 0.0036305828 0 0.0036305828 <NA>
## Q115610.fctr 0.0035255582 0 0.0035255582 <NA>
## Q116953.fctr 0.0029786716 0 0.0029786716 <NA>
## Q115602.fctr 0.0027844465 0 0.0027844465 <NA>
## Q100010.fctr 0.0024291540 0 0.0024291540 <NA>
## Q108617.fctr 0.0024119725 0 0.0024119725 <NA>
## Q100562.fctr 0.0017132769 0 0.0017132769 <NA>
## Q107491.fctr 0.0014031814 0 0.0014031814 <NA>
## Q114748.fctr 0.0008477228 0 0.0008477228 <NA>
## Q112478.fctr -0.0001517248 0 0.0001517248 <NA>
## Q103293.fctr -0.0005915534 0 0.0005915534 <NA>
## Q102674.fctr -0.0009759844 0 0.0009759844 <NA>
## Q108950.fctr -0.0010567028 0 0.0010567028 <NA>
## Q113584.fctr -0.0011387024 0 0.0011387024 <NA>
## Q102906.fctr -0.0011540297 0 0.0011540297 <NA>
## Q104996.fctr -0.0012202806 0 0.0012202806 <NA>
## Q116601.fctr -0.0022379241 0 0.0022379241 <NA>
## Q116448.fctr -0.0031731051 0 0.0031731051 <NA>
## Q106042.fctr -0.0032327194 0 0.0032327194 <NA>
## Q121011.fctr -0.0037329030 0 0.0037329030 <NA>
## .clusterid -0.0038495190 1 0.0038495190 <NA>
## .clusterid.fctr -0.0038495190 0 0.0038495190 <NA>
## Q113992.fctr -0.0041479796 0 0.0041479796 <NA>
## Q111220.fctr -0.0055758571 0 0.0055758571 <NA>
## Q124122.fctr -0.0061257448 0 0.0061257448 <NA>
## Q121700.fctr -0.0067756198 0 0.0067756198 <NA>
## Q114961.fctr -0.0079206587 0 0.0079206587 <NA>
## Q109367.fctr -0.0080456026 0 0.0080456026 <NA>
## Q120012.fctr -0.0084652930 0 0.0084652930 <NA>
## Q114517.fctr -0.0084741753 0 0.0084741753 <NA>
## Q119851.fctr -0.0093381833 0 0.0093381833 <NA>
## Q115390.fctr -0.0119300319 0 0.0119300319 <NA>
## Q102687.fctr -0.0120079165 0 0.0120079165 <NA>
## Q118892.fctr -0.0125250379 0 0.0125250379 <NA>
## YOB.Age.fctr -0.0129198495 0 0.0129198495 <NA>
## Q111848.fctr -0.0141099384 0 0.0141099384 <NA>
## Q108342.fctr -0.0151842510 0 0.0151842510 <NA>
## Q100680.fctr -0.0157762454 0 0.0157762454 Q100689.fctr
## Q114386.fctr -0.0168013326 0 0.0168013326 <NA>
## Q98059.fctr -0.0171637755 0 0.0171637755 Q98078.fctr
## Q102089.fctr -0.0174087944 0 0.0174087944 <NA>
## Q115195.fctr -0.0174522586 0 0.0174522586 <NA>
## Q113583.fctr -0.0191894717 0 0.0191894717 <NA>
## Q105840.fctr -0.0195569165 0 0.0195569165 <NA>
## Q121699.fctr -0.0196933075 0 0.0196933075 <NA>
## Q120379.fctr -0.0206291292 0 0.0206291292 <NA>
## Q99716.fctr -0.0209286674 0 0.0209286674 Q99480.fctr
## Q98078.fctr -0.0256516490 0 0.0256516490 <NA>
## Q100689.fctr -0.0256915080 0 0.0256915080 <NA>
## Q101163.fctr -0.0295046473 0 0.0295046473 <NA>
## Edn.fctr -0.0359295351 0 0.0359295351 <NA>
## Hhold.fctr -0.0511386673 0 0.0511386673 <NA>
## Q109244.fctr -0.1203812469 0 0.1203812469 <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Gender.fctr 1.561033 0.05387931 FALSE FALSE FALSE
## Q115611.fctr 1.194859 0.05387931 FALSE FALSE FALSE
## Q113181.fctr 1.006354 0.05387931 FALSE FALSE FALSE
## Q98197.fctr 1.129371 0.05387931 FALSE FALSE FALSE
## Q120472.fctr 1.292633 0.05387931 FALSE FALSE FALSE
## Q116881.fctr 1.010066 0.05387931 FALSE FALSE FALSE
## Q101596.fctr 1.041667 0.05387931 FALSE FALSE FALSE
## Q106272.fctr 1.116536 0.05387931 FALSE FALSE FALSE
## Q110740.fctr 1.050779 0.05387931 FALSE FALSE FALSE
## Q108855.fctr 1.273980 0.05387931 FALSE FALSE FALSE
## Q122771.fctr 1.414753 0.05387931 FALSE FALSE FALSE
## Q99480.fctr 1.225404 0.05387931 FALSE FALSE FALSE
## Q106388.fctr 1.065033 0.05387931 FALSE FALSE FALSE
## Q115899.fctr 1.197849 0.05387931 FALSE FALSE FALSE
## Q120014.fctr 1.044944 0.05387931 FALSE FALSE FALSE
## Q107869.fctr 1.211050 0.05387931 FALSE FALSE FALSE
## USER_ID 1.000000 100.00000000 FALSE FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE FALSE
## Q98869.fctr 1.080860 0.05387931 FALSE FALSE FALSE
## Q120650.fctr 1.896247 0.05387931 FALSE FALSE FALSE
## Q122769.fctr 1.060606 0.05387931 FALSE FALSE FALSE
## Q123621.fctr 1.466381 0.05387931 FALSE FALSE FALSE
## Q118117.fctr 1.174006 0.05387931 FALSE FALSE FALSE
## Q116441.fctr 1.019645 0.05387931 FALSE FALSE FALSE
## Q122120.fctr 1.297443 0.05387931 FALSE FALSE FALSE
## Q119334.fctr 1.081498 0.05387931 FALSE FALSE FALSE
## Q106993.fctr 1.327392 0.05387931 FALSE FALSE FALSE
## Q105655.fctr 1.079316 0.05387931 FALSE FALSE FALSE
## Q117186.fctr 1.053878 0.05387931 FALSE FALSE FALSE
## Q122770.fctr 1.008802 0.05387931 FALSE FALSE FALSE
## Q114152.fctr 1.027617 0.05387931 FALSE FALSE FALSE
## Q120194.fctr 1.016716 0.05387931 FALSE FALSE FALSE
## Q118232.fctr 1.365812 0.05387931 FALSE FALSE FALSE
## Income.fctr 1.256724 0.12571839 FALSE FALSE FALSE
## Q116197.fctr 1.073778 0.05387931 FALSE FALSE FALSE
## Q102289.fctr 1.033482 0.05387931 FALSE FALSE FALSE
## Q118233.fctr 1.199142 0.05387931 FALSE FALSE FALSE
## Q108856.fctr 1.080645 0.05387931 FALSE FALSE FALSE
## Q99982.fctr 1.339380 0.05387931 FALSE FALSE FALSE
## Q117193.fctr 1.140665 0.05387931 FALSE FALSE FALSE
## Q123464.fctr 1.326681 0.05387931 FALSE FALSE FALSE
## Q111580.fctr 1.024977 0.05387931 FALSE FALSE FALSE
## Q119650.fctr 1.456978 0.05387931 FALSE FALSE FALSE
## Q118237.fctr 1.088017 0.05387931 FALSE FALSE FALSE
## YOB 1.027559 1.41882184 FALSE FALSE FALSE
## Q112270.fctr 1.254284 0.05387931 FALSE FALSE FALSE
## Q116797.fctr 1.009589 0.05387931 FALSE FALSE FALSE
## Q124742.fctr 2.565379 0.05387931 FALSE FALSE FALSE
## Q99581.fctr 1.375000 0.05387931 FALSE FALSE FALSE
## Q115777.fctr 1.140288 0.05387931 FALSE FALSE FALSE
## Q101162.fctr 1.103229 0.05387931 FALSE FALSE FALSE
## Q98578.fctr 1.093556 0.05387931 FALSE FALSE FALSE
## Q108754.fctr 1.008090 0.05387931 FALSE FALSE FALSE
## .rnorm 1.000000 100.00000000 FALSE FALSE FALSE
## Q106389.fctr 1.341307 0.05387931 FALSE FALSE TRUE
## Q96024.fctr 1.144428 0.05387931 FALSE FALSE TRUE
## Q108343.fctr 1.064910 0.05387931 FALSE FALSE TRUE
## Q112512.fctr 1.299253 0.05387931 FALSE FALSE TRUE
## Q120978.fctr 1.131963 0.05387931 FALSE FALSE TRUE
## Q106997.fctr 1.177632 0.05387931 FALSE FALSE TRUE
## YOB.Age.dff 1.007778 0.35919540 FALSE FALSE TRUE
## Q115610.fctr 1.359695 0.05387931 FALSE FALSE TRUE
## Q116953.fctr 1.039180 0.05387931 FALSE FALSE TRUE
## Q115602.fctr 1.322302 0.05387931 FALSE FALSE TRUE
## Q100010.fctr 1.268156 0.05387931 FALSE FALSE TRUE
## Q108617.fctr 1.390618 0.05387931 FALSE FALSE TRUE
## Q100562.fctr 1.217215 0.05387931 FALSE FALSE TRUE
## Q107491.fctr 1.419021 0.05387931 FALSE FALSE TRUE
## Q114748.fctr 1.051125 0.05387931 FALSE FALSE TRUE
## Q112478.fctr 1.113648 0.05387931 FALSE FALSE TRUE
## Q103293.fctr 1.122287 0.05387931 FALSE FALSE TRUE
## Q102674.fctr 1.073412 0.05387931 FALSE FALSE TRUE
## Q108950.fctr 1.103872 0.05387931 FALSE FALSE TRUE
## Q113584.fctr 1.212486 0.05387931 FALSE FALSE TRUE
## Q102906.fctr 1.053396 0.05387931 FALSE FALSE TRUE
## Q104996.fctr 1.173840 0.05387931 FALSE FALSE TRUE
## Q116601.fctr 1.394914 0.05387931 FALSE FALSE TRUE
## Q116448.fctr 1.161031 0.05387931 FALSE FALSE TRUE
## Q106042.fctr 1.247738 0.05387931 FALSE FALSE TRUE
## Q121011.fctr 1.153676 0.05387931 FALSE FALSE TRUE
## .clusterid 1.690432 0.05387931 FALSE FALSE TRUE
## .clusterid.fctr 1.690432 0.05387931 FALSE FALSE TRUE
## Q113992.fctr 1.267442 0.05387931 FALSE FALSE TRUE
## Q111220.fctr 1.262849 0.05387931 FALSE FALSE TRUE
## Q124122.fctr 1.412807 0.05387931 FALSE FALSE TRUE
## Q121700.fctr 1.708221 0.05387931 FALSE FALSE TRUE
## Q114961.fctr 1.250436 0.05387931 FALSE FALSE FALSE
## Q109367.fctr 1.008571 0.05387931 FALSE FALSE FALSE
## Q120012.fctr 1.047185 0.05387931 FALSE FALSE FALSE
## Q114517.fctr 1.183374 0.05387931 FALSE FALSE FALSE
## Q119851.fctr 1.244519 0.05387931 FALSE FALSE FALSE
## Q115390.fctr 1.150505 0.05387931 FALSE FALSE FALSE
## Q102687.fctr 1.256545 0.05387931 FALSE FALSE FALSE
## Q118892.fctr 1.347380 0.05387931 FALSE FALSE FALSE
## YOB.Age.fctr 1.005794 0.16163793 FALSE FALSE FALSE
## Q111848.fctr 1.113602 0.05387931 FALSE FALSE FALSE
## Q108342.fctr 1.048292 0.05387931 FALSE FALSE FALSE
## Q100680.fctr 1.102386 0.05387931 FALSE FALSE FALSE
## Q114386.fctr 1.092072 0.05387931 FALSE FALSE FALSE
## Q98059.fctr 1.493810 0.05387931 FALSE FALSE FALSE
## Q102089.fctr 1.055963 0.05387931 FALSE FALSE FALSE
## Q115195.fctr 1.065496 0.05387931 FALSE FALSE FALSE
## Q113583.fctr 1.102515 0.05387931 FALSE FALSE FALSE
## Q105840.fctr 1.275362 0.05387931 FALSE FALSE FALSE
## Q121699.fctr 1.507127 0.05387931 FALSE FALSE FALSE
## Q120379.fctr 1.046326 0.05387931 FALSE FALSE FALSE
## Q99716.fctr 1.328693 0.05387931 FALSE FALSE FALSE
## Q98078.fctr 1.266595 0.05387931 FALSE FALSE FALSE
## Q100689.fctr 1.029800 0.05387931 FALSE FALSE FALSE
## Q101163.fctr 1.327394 0.05387931 FALSE FALSE FALSE
## Edn.fctr 1.392610 0.14367816 FALSE FALSE FALSE
## Hhold.fctr 1.525094 0.12571839 FALSE FALSE FALSE
## Q109244.fctr 1.125916 0.05387931 FALSE FALSE FALSE## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## [1] cor.y exclude.as.feat cor.y.abs cor.high.X
## [5] freqRatio percentUnique zeroVar nzv
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## YOB.Age.dff
## 438
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024 .lcn
## 2836 2858 1392## [1] "glb_feats_df:"## [1] 113 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID 0.03023049 TRUE 0.03023049 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID <NA> NA FALSE TRUE
## Party.fctr <NA> NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 1 select.features 1 0 0 5.859 13.632 7.773
## 2 fit.models 2 0 0 13.633 NA NAStep 2.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 14.058 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 14.058 14.081
## 2 fit.models_0_MFO 1 1 myMFO_classfr 14.082 NA
## elapsed
## 1 0.023
## 2 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.386000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
## D R
## 0.5299798 0.4700202
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.675000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.676000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5299798 0.4700202
## 2 0.5299798 0.4700202
## 3 0.5299798 0.4700202
## 4 0.5299798 0.4700202
## 5 0.5299798 0.4700202
## 6 0.5299798 0.4700202## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk
## Prediction
## Reference D R
## D 2360 0
## R 2093 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5299798 0.0000000 0.5151927 0.5447275 0.5299798
## AccuracyPValue McnemarPValue
## 0.5061085 0.0000000
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5299798 0.4700202
## 2 0.5299798 0.4700202
## 3 0.5299798 0.4700202
## 4 0.5299798 0.4700202
## 5 0.5299798 0.4700202
## 6 0.5299798 0.4700202
## Prediction
## Reference D R
## D 591 0
## R 524 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.300448e-01 0.000000e+00 5.002547e-01 5.596760e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 5.122058e-01 1.552393e-115
## [1] "myfit_mdl: predict complete: 7.876000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.28
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.002 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5299798
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5151927 0.5447275 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5300448
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5002547 0.559676 0
## [1] "myfit_mdl: exit: 7.884000 secs"
## label step_major step_minor label_minor bgn end
## 2 fit.models_0_MFO 1 1 myMFO_classfr 14.082 21.969
## 3 fit.models_0_Random 1 2 myrandom_classfr 21.970 NA
## elapsed
## 2 7.887
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.407000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.677000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.679000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 2360 0
## R 2093 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5299798 0.0000000 0.5151927 0.5447275 0.5299798
## AccuracyPValue McnemarPValue
## 0.5061085 0.0000000
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 591 0
## R 524 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.300448e-01 0.000000e+00 5.002547e-01 5.596760e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 5.122058e-01 1.552393e-115
## [1] "myfit_mdl: predict complete: 8.979000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.265 0.002 0.4853405
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5182203 0.4524606 0.4907312 0.55
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.5299798 0.5151927
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5447275 0 0.4836608 0.5093063
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.4580153 0.5181895 0.55 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5300448 0.5002547 0.559676
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 8.990000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 21.970 30.972 9.002
## 4 30.972 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.669000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.00303 on full training set
## [1] "myfit_mdl: train complete: 1.420000 secs"
## Length Class Mode
## a0 64 -none- numeric
## beta 256 dgCMatrix S4
## df 64 -none- numeric
## dim 2 -none- numeric
## lambda 64 -none- numeric
## dev.ratio 64 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Q109244.fctrNo
## -0.22457950 -0.07842278 0.17207768 0.55035173
## Q109244.fctrYes
## -1.62220258
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Gender.fctrF" "Gender.fctrM" "Q109244.fctrNo"
## [5] "Q109244.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.521000 secs"
## Prediction
## Reference D R
## D 1082 1278
## R 396 1697
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.240737e-01 2.630030e-01 6.096574e-01 6.383270e-01 5.299798e-01
## AccuracyPValue McnemarPValue
## 5.498732e-37 7.694458e-103
## Prediction
## Reference D R
## D 591 0
## R 524 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.300448e-01 0.000000e+00 5.002547e-01 5.596760e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 5.122058e-01 1.552393e-115
## [1] "myfit_mdl: predict complete: 9.385000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.742 0.062 0.6195812
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.6720339 0.5671285 0.6728307 0.45
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6696922 0.6240737 0.6096574
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.638327 0.263003 0.4999322 0.5532995
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.4465649 0.5102459 0.65 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5300448 0.5002547 0.559676
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 9.397000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.691000 secs"## Loading required package: rpart## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0336 on full training set
## [1] "myfit_mdl: train complete: 2.572000 secs"## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4453
##
## CP nsplit rel error
## 1 0.13425705 0 1.0000000
## 2 0.06306737 1 0.8657430
## 3 0.03356426 2 0.8026756
##
## Variable importance
## Q109244.fctrYes Q109244.fctrNo Gender.fctrM Gender.fctrF
## 83 15 1 1
##
## Node number 1: 4453 observations, complexity param=0.134257
## predicted class=D expected loss=0.4700202 P(node) =1
## class counts: 2360 2093
## probabilities: 0.530 0.470
## left son=2 (746 obs) right son=3 (3707 obs)
## Primary splits:
## Q109244.fctrYes < 0.5 to the right, improve=203.92180, (0 missing)
## Q109244.fctrNo < 0.5 to the left, improve=128.26260, (0 missing)
## Gender.fctrF < 0.5 to the right, improve= 35.40579, (0 missing)
## Gender.fctrM < 0.5 to the left, improve= 34.78869, (0 missing)
##
## Node number 2: 746 observations
## predicted class=D expected loss=0.1327078 P(node) =0.1675275
## class counts: 647 99
## probabilities: 0.867 0.133
##
## Node number 3: 3707 observations, complexity param=0.06306737
## predicted class=R expected loss=0.4620987 P(node) =0.8324725
## class counts: 1713 1994
## probabilities: 0.462 0.538
## left son=6 (1746 obs) right son=7 (1961 obs)
## Primary splits:
## Q109244.fctrNo < 0.5 to the left, improve=37.829750, (0 missing)
## Gender.fctrM < 0.5 to the left, improve=10.392830, (0 missing)
## Gender.fctrF < 0.5 to the right, improve= 9.281051, (0 missing)
## Surrogate splits:
## Gender.fctrM < 0.5 to the left, agree=0.564, adj=0.073, (0 split)
## Gender.fctrF < 0.5 to the right, agree=0.557, adj=0.060, (0 split)
##
## Node number 6: 1746 observations
## predicted class=D expected loss=0.4621993 P(node) =0.3920952
## class counts: 939 807
## probabilities: 0.538 0.462
##
## Node number 7: 1961 observations
## predicted class=R expected loss=0.3946966 P(node) =0.4403773
## class counts: 774 1187
## probabilities: 0.395 0.605
##
## n= 4453
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4453 2093 D (0.5299798 0.4700202)
## 2) Q109244.fctrYes>=0.5 746 99 D (0.8672922 0.1327078) *
## 3) Q109244.fctrYes< 0.5 3707 1713 R (0.4620987 0.5379013)
## 6) Q109244.fctrNo< 0.5 1746 807 D (0.5378007 0.4621993) *
## 7) Q109244.fctrNo>=0.5 1961 774 R (0.3946966 0.6053034) *
## [1] "myfit_mdl: train diagnostics complete: 3.506000 secs"
## Prediction
## Reference D R
## D 1586 774
## R 906 1187
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.227263e-01 2.400187e-01 6.083009e-01 6.369905e-01 5.299798e-01
## AccuracyPValue McnemarPValue
## 5.567661e-36 1.393120e-03
## Prediction
## Reference D R
## D 591 0
## R 524 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.300448e-01 0.000000e+00 5.002547e-01 5.596760e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 5.122058e-01 1.552393e-115
## [1] "myfit_mdl: predict complete: 10.743000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.873 0.02 0.6195812
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.6720339 0.5671285 0.6630238 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.5855945 0.6227308 0.6083009
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.6369905 0.2400218 0.4999322 0.5532995
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.4465649 0.5000646 0.65 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5300448 0.5002547 0.559676
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.0111662 0.02269692
## [1] "myfit_mdl: exit: 10.757000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 30.972 51.175 20.203
## 5 51.176 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr,Q109244.fctr:Q106272.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q98078.fctr"
## [1] "myfit_mdl: setup complete: 0.706000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00303 on full training set
## [1] "myfit_mdl: train complete: 6.591000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 76 -none- numeric
## beta 3952 dgCMatrix S4
## df 76 -none- numeric
## dim 2 -none- numeric
## lambda 76 -none- numeric
## dev.ratio 76 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 52 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.3012166986 -0.0109382085
## Gender.fctrM Q109244.fctrNo
## 0.1708984440 0.3868107662
## Q109244.fctrYes Q109244.fctrNA:Q100689.fctrNo
## -1.3559420661 -0.2517626984
## Q109244.fctrYes:Q100689.fctrNo Q109244.fctrNA:Q100689.fctrYes
## 0.5102484371 -0.4307487983
## Q109244.fctrNo:Q100689.fctrYes Q109244.fctrYes:Q100689.fctrYes
## -0.1685326641 0.0760870425
## Q109244.fctrNA:Q106272.fctrNo Q109244.fctrNo:Q106272.fctrNo
## 0.0289683642 -0.1172934905
## Q109244.fctrYes:Q106272.fctrNo Q109244.fctrNA:Q106272.fctrYes
## 0.0756236038 0.2819554980
## Q109244.fctrNo:Q106272.fctrYes Q109244.fctrYes:Q106272.fctrYes
## 0.1457027794 0.0007338447
## Q109244.fctrNA:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm...
## 0.1552561122 0.1471464007
## Q109244.fctrNA:Q108855.fctrYes! Q109244.fctrNo:Q108855.fctrYes!
## 0.1296927610 0.2888189003
## Q109244.fctrNA:Q120472.fctrArt Q109244.fctrNo:Q120472.fctrArt
## 0.0689142696 0.0750361900
## Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience
## -0.3289937353 0.1754476871
## Q109244.fctrNo:Q120472.fctrScience Q109244.fctrYes:Q120472.fctrScience
## 0.2138529089 -0.2475507185
## Q109244.fctrNA:Q122771.fctrPc Q109244.fctrNo:Q122771.fctrPc
## -0.0079396921 0.0966690797
## Q109244.fctrYes:Q122771.fctrPc Q109244.fctrNA:Q122771.fctrPt
## -0.4790145160 0.3778745219
## Q109244.fctrNo:Q122771.fctrPt Q109244.fctrYes:Q122771.fctrPt
## 0.3710194029 -0.4049929837
## Q109244.fctrNA:Q123621.fctrNo Q109244.fctrNo:Q123621.fctrNo
## -0.0410496589 -0.1237008521
## Q109244.fctrYes:Q123621.fctrNo Q109244.fctrNA:Q123621.fctrYes
## 0.1384441851 0.0147708073
## Q109244.fctrNo:Q123621.fctrYes Q109244.fctrYes:Q123621.fctrYes
## 0.0657429643 -0.0092805203
## Q109244.fctrNA:Q98078.fctrNo Q109244.fctrNo:Q98078.fctrNo
## -0.4001842495 -0.0612726784
## Q109244.fctrYes:Q98078.fctrNo Q109244.fctrNA:Q98078.fctrYes
## -0.0607305113 -0.3679782893
## Q109244.fctrNo:Q98078.fctrYes Q109244.fctrYes:Q98078.fctrYes
## -0.0834582183 -0.5135398377
## Q109244.fctrNA:Q99480.fctrNo Q109244.fctrNo:Q99480.fctrNo
## -0.2017713747 -0.3655635651
## Q109244.fctrYes:Q99480.fctrNo Q109244.fctrNA:Q99480.fctrYes
## 0.1647133403 0.4508014855
## Q109244.fctrNo:Q99480.fctrYes Q109244.fctrYes:Q99480.fctrYes
## -0.0265800178 0.2720263279
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.30366434 -0.00845797
## Gender.fctrM Q109244.fctrNo
## 0.17346996 0.38994555
## Q109244.fctrYes Q109244.fctrNA:Q100689.fctrNo
## -1.37949852 -0.25469033
## Q109244.fctrYes:Q100689.fctrNo Q109244.fctrNA:Q100689.fctrYes
## 0.53006725 -0.43389725
## Q109244.fctrNo:Q100689.fctrYes Q109244.fctrYes:Q100689.fctrYes
## -0.16964229 0.09462114
## Q109244.fctrNA:Q106272.fctrNo Q109244.fctrNo:Q106272.fctrNo
## 0.03150681 -0.11721709
## Q109244.fctrYes:Q106272.fctrNo Q109244.fctrNA:Q106272.fctrYes
## 0.08519571 0.28374701
## Q109244.fctrNo:Q106272.fctrYes Q109244.fctrYes:Q106272.fctrYes
## 0.14647871 0.01055063
## Q109244.fctrNA:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm...
## 0.15632229 0.15162053
## Q109244.fctrNA:Q108855.fctrYes! Q109244.fctrNo:Q108855.fctrYes!
## 0.13069025 0.28931414
## Q109244.fctrNA:Q120472.fctrArt Q109244.fctrNo:Q120472.fctrArt
## 0.07003770 0.07699007
## Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience
## -0.33115217 0.17647368
## Q109244.fctrNo:Q120472.fctrScience Q109244.fctrYes:Q120472.fctrScience
## 0.21590674 -0.25068557
## Q109244.fctrNA:Q122771.fctrPc Q109244.fctrNo:Q122771.fctrPc
## -0.00882646 0.09902559
## Q109244.fctrYes:Q122771.fctrPc Q109244.fctrNA:Q122771.fctrPt
## -0.49349467 0.37863745
## Q109244.fctrNo:Q122771.fctrPt Q109244.fctrYes:Q122771.fctrPt
## 0.37361880 -0.42018925
## Q109244.fctrNA:Q123621.fctrNo Q109244.fctrNo:Q123621.fctrNo
## -0.04166767 -0.12679505
## Q109244.fctrYes:Q123621.fctrNo Q109244.fctrNA:Q123621.fctrYes
## 0.15101206 0.01512027
## Q109244.fctrNo:Q123621.fctrYes Q109244.fctrNA:Q98078.fctrNo
## 0.06357346 -0.40308902
## Q109244.fctrNo:Q98078.fctrNo Q109244.fctrYes:Q98078.fctrNo
## -0.06365618 -0.07464880
## Q109244.fctrNA:Q98078.fctrYes Q109244.fctrNo:Q98078.fctrYes
## -0.37084157 -0.08604922
## Q109244.fctrYes:Q98078.fctrYes Q109244.fctrNA:Q99480.fctrNo
## -0.52839369 -0.20039866
## Q109244.fctrNo:Q99480.fctrNo Q109244.fctrYes:Q99480.fctrNo
## -0.36899083 0.17831028
## Q109244.fctrNA:Q99480.fctrYes Q109244.fctrNo:Q99480.fctrYes
## 0.45411659 -0.02955152
## Q109244.fctrYes:Q99480.fctrYes
## 0.28589434
## [1] "myfit_mdl: train diagnostics complete: 7.619000 secs"
## Prediction
## Reference D R
## D 1489 871
## R 724 1369
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.418145e-01 2.838894e-01 6.275303e-01 6.559125e-01 5.299798e-01
## AccuracyPValue McnemarPValue
## 1.275186e-51 2.564646e-04
## Prediction
## Reference D R
## D 572 19
## R 500 24
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.345291e-01 1.440199e-02 5.047442e-01 5.641315e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 3.937954e-01 1.510069e-98
## [1] "myfit_mdl: predict complete: 16.921000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 Q109244.fctr,Gender.fctr,Q109244.fctr:Q106272.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q98078.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 5.857 0.43
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6425086 0.6309322 0.654085 0.6985067
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.6318948 0.6250526
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6275303 0.6559125 0.2491892
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5218093 0.5245347 0.519084 0.5242069
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.08465608 0.5345291
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5047442 0.5641315 0.01440199
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01599098 0.03290196
## [1] "myfit_mdl: exit: 16.935000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 51.176 68.14 16.964
## 6 68.141 NA NAindepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.671000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.014 on full training set
## [1] "myfit_mdl: train complete: 24.602000 secs"
## Length Class Mode
## a0 84 -none- numeric
## beta 20748 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 247 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.231235208 0.105996862
## Hhold.fctrMKy Hhold.fctrPKn
## 0.037530839 -0.462338411
## Income.fctr.Q Income.fctr.C
## 0.050511902 0.018998965
## Q100689.fctrYes Q101163.fctrDad
## -0.055283639 0.146113017
## Q101596.fctrYes Q102089.fctrRent
## 0.036618639 -0.015340306
## Q106042.fctrNo Q106272.fctrYes
## 0.011463411 0.009424122
## Q106388.fctrYes Q106997.fctrGr
## 0.026714803 0.075133557
## Q108855.fctrYes! Q109244.fctrNo
## 0.029034122 0.424732820
## Q109244.fctrYes Q110740.fctrPC
## -1.290091615 0.083252496
## Q112478.fctrNo Q113181.fctrNo
## 0.010843412 -0.088149365
## Q113181.fctrYes Q115195.fctrYes
## 0.187631356 -0.026839083
## Q115611.fctrNo Q115611.fctrYes
## -0.142804586 0.238183824
## Q115899.fctrCs Q116881.fctrHappy
## -0.090071561 -0.008298821
## Q116881.fctrRight Q116953.fctrNo
## 0.093162156 0.028952204
## Q118232.fctrId Q119851.fctrNo
## -0.016361288 0.100215288
## Q120379.fctrYes Q120472.fctrScience
## -0.104208571 0.057685089
## Q120650.fctrYes Q122771.fctrPt
## 0.046857175 0.073870604
## Q96024.fctrNo Q98197.fctrNo
## -0.015142098 -0.317900131
## Q98197.fctrYes Q98869.fctrNo
## 0.019623012 -0.054425151
## Q99480.fctrNo Q109244.fctrYes:.clusterid.fctr3
## -0.060237559 -0.340923455
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.013105017
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.2330609616 0.1054981329
## Hhold.fctrMKy Hhold.fctrPKn
## 0.0441815228 -0.4832355082
## Income.fctr.Q Income.fctr.C
## 0.0602852373 0.0347677155
## Q100689.fctrYes Q101163.fctrDad
## -0.0683272189 0.1533257769
## Q101596.fctrYes Q102089.fctrRent
## 0.0439390076 -0.0239022190
## Q104996.fctrNo Q106042.fctrNo
## 0.0043630800 0.0181892131
## Q106272.fctrYes Q106388.fctrYes
## 0.0133561866 0.0322154731
## Q106389.fctrNo Q106997.fctrGr
## 0.0034398840 0.0883487716
## Q108342.fctrOnline Q108855.fctrYes!
## -0.0024951688 0.0384230419
## Q109244.fctrNo Q109244.fctrYes
## 0.4252674971 -1.2944252294
## Q110740.fctrPC Q112478.fctrNo
## 0.0904838371 0.0206308499
## Q113181.fctrNo Q113181.fctrYes
## -0.0974065367 0.1868282743
## Q113583.fctrTunes Q115195.fctrYes
## -0.0004141873 -0.0375439703
## Q115611.fctrNo Q115611.fctrYes
## -0.1393412913 0.2431770094
## Q115899.fctrCs Q116881.fctrHappy
## -0.1011804925 -0.0184386866
## Q116881.fctrRight Q116953.fctrNo
## 0.0961084981 0.0426058122
## Q118232.fctrId Q119851.fctrNo
## -0.0266978568 0.1074911511
## Q119851.fctrYes Q120379.fctrYes
## -0.0033132601 -0.1185959917
## Q120472.fctrScience Q120650.fctrYes
## 0.0652028941 0.0591120125
## Q121699.fctrYes Q122120.fctrYes
## -0.0080339783 0.0008435008
## Q122771.fctrPt Q124742.fctrNo
## 0.0876713081 -0.0112811758
## Q96024.fctrNo Q98197.fctrNo
## -0.0246782767 -0.3250116396
## Q98197.fctrYes Q98869.fctrNo
## 0.0194484440 -0.0664120602
## Q99480.fctrNo Q99480.fctrYes
## -0.0685669208 0.0052925176
## Q109244.fctrYes:.clusterid.fctr3 YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.3783124405 -0.0181184935
## [1] "myfit_mdl: train diagnostics complete: 25.250000 secs"
## Prediction
## Reference D R
## D 1671 689
## R 807 1286
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.640467e-01 3.235116e-01 6.499576e-01 6.779203e-01 5.299798e-01
## AccuracyPValue McnemarPValue
## 1.083627e-73 2.486636e-03
## Prediction
## Reference D R
## D 440 151
## R 321 203
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.766816e-01 1.343766e-01 5.470616e-01 6.058959e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 9.762675e-04 7.318358e-15
## [1] "myfit_mdl: predict complete: 40.773000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 23.803 2.165
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6612399 0.7080508 0.614429 0.7306715
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.6322517 0.6471308
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6499576 0.6779203 0.2907403
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.558818 0.6023689 0.5152672 0.5757966
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.55 0.4624146 0.5766816
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5470616 0.6058959 0.1343766
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.00812887 0.01734997
## [1] "myfit_mdl: exit: 40.787000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 68.141 108.978
## 7 fit.models_0_end 1 6 teardown 108.979 NA
## elapsed
## 6 40.838
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 2 fit.models 2 0 0 13.633 108.993 95.36
## 3 fit.models 2 1 1 108.993 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 113.962 NA NA# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indepVar <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
indepVar <- setdiff(indepVar, topindep_var)
if (length(interact_vars) > 0) {
indepVar <-
setdiff(indepVar, myextract_actual_feats(interact_vars))
indepVar <- c(indepVar,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indepVar <- union(indepVar, topindep_var)
}
}
if (is.null(indepVar))
indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indepVar))
indepVar <- mygetIndepVar(glb_feats_df)
if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {
indepVar <-
eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
}
indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indepVar <- setdiff(indepVar, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indepVar]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 113.962 113.971
## 2 fit.models_1_All.X 1 1 setup 113.972 NA
## elapsed
## 1 0.01
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 113.972 113.98
## 3 fit.models_1_All.X 1 2 glmnet 113.980 NA
## elapsed
## 2 0.008
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.728000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.014 on full training set
## [1] "myfit_mdl: train complete: 23.552000 secs"
## Length Class Mode
## a0 84 -none- numeric
## beta 20748 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 247 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.231235208 0.105996862
## Hhold.fctrMKy Hhold.fctrPKn
## 0.037530839 -0.462338411
## Income.fctr.Q Income.fctr.C
## 0.050511902 0.018998965
## Q100689.fctrYes Q101163.fctrDad
## -0.055283639 0.146113017
## Q101596.fctrYes Q102089.fctrRent
## 0.036618639 -0.015340306
## Q106042.fctrNo Q106272.fctrYes
## 0.011463411 0.009424122
## Q106388.fctrYes Q106997.fctrGr
## 0.026714803 0.075133557
## Q108855.fctrYes! Q109244.fctrNo
## 0.029034122 0.424732820
## Q109244.fctrYes Q110740.fctrPC
## -1.290091615 0.083252496
## Q112478.fctrNo Q113181.fctrNo
## 0.010843412 -0.088149365
## Q113181.fctrYes Q115195.fctrYes
## 0.187631356 -0.026839083
## Q115611.fctrNo Q115611.fctrYes
## -0.142804586 0.238183824
## Q115899.fctrCs Q116881.fctrHappy
## -0.090071561 -0.008298821
## Q116881.fctrRight Q116953.fctrNo
## 0.093162156 0.028952204
## Q118232.fctrId Q119851.fctrNo
## -0.016361288 0.100215288
## Q120379.fctrYes Q120472.fctrScience
## -0.104208571 0.057685089
## Q120650.fctrYes Q122771.fctrPt
## 0.046857175 0.073870604
## Q96024.fctrNo Q98197.fctrNo
## -0.015142098 -0.317900131
## Q98197.fctrYes Q98869.fctrNo
## 0.019623012 -0.054425151
## Q99480.fctrNo Q109244.fctrYes:.clusterid.fctr3
## -0.060237559 -0.340923455
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.013105017
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.2330609616 0.1054981329
## Hhold.fctrMKy Hhold.fctrPKn
## 0.0441815228 -0.4832355082
## Income.fctr.Q Income.fctr.C
## 0.0602852373 0.0347677155
## Q100689.fctrYes Q101163.fctrDad
## -0.0683272189 0.1533257769
## Q101596.fctrYes Q102089.fctrRent
## 0.0439390076 -0.0239022190
## Q104996.fctrNo Q106042.fctrNo
## 0.0043630800 0.0181892131
## Q106272.fctrYes Q106388.fctrYes
## 0.0133561866 0.0322154731
## Q106389.fctrNo Q106997.fctrGr
## 0.0034398840 0.0883487716
## Q108342.fctrOnline Q108855.fctrYes!
## -0.0024951688 0.0384230419
## Q109244.fctrNo Q109244.fctrYes
## 0.4252674971 -1.2944252294
## Q110740.fctrPC Q112478.fctrNo
## 0.0904838371 0.0206308499
## Q113181.fctrNo Q113181.fctrYes
## -0.0974065367 0.1868282743
## Q113583.fctrTunes Q115195.fctrYes
## -0.0004141873 -0.0375439703
## Q115611.fctrNo Q115611.fctrYes
## -0.1393412913 0.2431770094
## Q115899.fctrCs Q116881.fctrHappy
## -0.1011804925 -0.0184386866
## Q116881.fctrRight Q116953.fctrNo
## 0.0961084981 0.0426058122
## Q118232.fctrId Q119851.fctrNo
## -0.0266978568 0.1074911511
## Q119851.fctrYes Q120379.fctrYes
## -0.0033132601 -0.1185959917
## Q120472.fctrScience Q120650.fctrYes
## 0.0652028941 0.0591120125
## Q121699.fctrYes Q122120.fctrYes
## -0.0080339783 0.0008435008
## Q122771.fctrPt Q124742.fctrNo
## 0.0876713081 -0.0112811758
## Q96024.fctrNo Q98197.fctrNo
## -0.0246782767 -0.3250116396
## Q98197.fctrYes Q98869.fctrNo
## 0.0194484440 -0.0664120602
## Q99480.fctrNo Q99480.fctrYes
## -0.0685669208 0.0052925176
## Q109244.fctrYes:.clusterid.fctr3 YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.3783124405 -0.0181184935
## [1] "myfit_mdl: train diagnostics complete: 24.275000 secs"
## Prediction
## Reference D R
## D 1671 689
## R 807 1286
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.640467e-01 3.235116e-01 6.499576e-01 6.779203e-01 5.299798e-01
## AccuracyPValue McnemarPValue
## 1.083627e-73 2.486636e-03
## Prediction
## Reference D R
## D 440 151
## R 321 203
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.766816e-01 1.343766e-01 5.470616e-01 6.058959e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 9.762675e-04 7.318358e-15
## [1] "myfit_mdl: predict complete: 39.496000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 22.7 2.148
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6612399 0.7080508 0.614429 0.7306715
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.6322517 0.6471308
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6499576 0.6779203 0.2907403
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.558818 0.6023689 0.5152672 0.5757966
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.55 0.4624146 0.5766816
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5470616 0.6058959 0.1343766
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.00812887 0.01734997
## [1] "myfit_mdl: exit: 39.511000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 113.980 153.496
## 4 fit.models_1_All.X 1 3 glm 153.497 NA
## elapsed
## 3 39.516
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] " indepVar: Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.713000 secs"
## + Fold1.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 15.424000 secs"
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2555 -1.0128 -0.3356 1.0094 2.7812
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2294821 0.2867308 -0.800 0.423514
## .rnorm -0.0056711 0.0344912 -0.164 0.869400
## Edn.fctr.L -0.0593937 0.1595454 -0.372 0.709693
## Edn.fctr.Q 0.0165486 0.1497805 0.110 0.912024
## Edn.fctr.C 0.1052661 0.1320747 0.797 0.425440
## `Edn.fctr^4` 0.0458746 0.1322264 0.347 0.728637
## `Edn.fctr^5` 0.1292772 0.1208350 1.070 0.284680
## `Edn.fctr^6` -0.0640205 0.1083013 -0.591 0.554431
## `Edn.fctr^7` -0.1422989 0.1204544 -1.181 0.237464
## Gender.fctrF 0.1594986 0.2514122 0.634 0.525813
## Gender.fctrM 0.3035347 0.2472810 1.227 0.219639
## Hhold.fctrMKn -0.0939331 0.1871601 -0.502 0.615748
## Hhold.fctrMKy 0.0161835 0.1742481 0.093 0.926002
## Hhold.fctrPKn -0.8500729 0.2605185 -3.263 0.001102
## Hhold.fctrPKy -0.0257350 0.3498504 -0.074 0.941360
## Hhold.fctrSKn -0.2065902 0.1476651 -1.399 0.161799
## Hhold.fctrSKy -0.2429270 0.2415073 -1.006 0.314474
## Income.fctr.L 0.0380172 0.1114270 0.341 0.732964
## Income.fctr.Q 0.2020647 0.1024540 1.972 0.048581
## Income.fctr.C 0.2304328 0.0998934 2.307 0.021067
## `Income.fctr^4` 0.0999662 0.0954676 1.047 0.295043
## `Income.fctr^5` 0.0771338 0.0973155 0.793 0.428002
## `Income.fctr^6` -0.0912493 0.0949286 -0.961 0.336431
## Q100010.fctrNo -0.4464090 0.2331209 -1.915 0.055502
## Q100010.fctrYes -0.3926010 0.2134461 -1.839 0.065864
## Q100562.fctrNo 0.1664220 0.2062758 0.807 0.419785
## Q100562.fctrYes 0.0698981 0.1826257 0.383 0.701913
## Q100680.fctrNo 0.1318517 0.2045709 0.645 0.519233
## Q100680.fctrYes 0.2265799 0.1981872 1.143 0.252930
## Q100689.fctrNo -0.2873354 0.1990454 -1.444 0.148861
## Q100689.fctrYes -0.5097684 0.1987023 -2.565 0.010303
## Q101162.fctrOptimist -0.1473685 0.1891400 -0.779 0.435891
## Q101162.fctrPessimist -0.0964173 0.1940601 -0.497 0.619300
## Q101163.fctrDad 0.2463050 0.1677344 1.468 0.141989
## Q101163.fctrMom 0.0244619 0.1721267 0.142 0.886989
## Q101596.fctrNo 0.6490527 0.1723880 3.765 0.000167
## Q101596.fctrYes 0.7357512 0.1808205 4.069 4.72e-05
## Q102089.fctrOwn -0.1998636 0.1708576 -1.170 0.242095
## Q102089.fctrRent -0.2717482 0.1807399 -1.504 0.132702
## Q102289.fctrNo 0.0978987 0.1767139 0.554 0.579582
## Q102289.fctrYes 0.0410283 0.1868574 0.220 0.826206
## Q102674.fctrNo 0.4163676 0.2242413 1.857 0.063342
## Q102674.fctrYes 0.2737607 0.2359439 1.160 0.245935
## Q102687.fctrNo -0.4172661 0.2380127 -1.753 0.079580
## Q102687.fctrYes -0.3485480 0.2355009 -1.480 0.138866
## Q102906.fctrNo -0.0653042 0.1766590 -0.370 0.711634
## Q102906.fctrYes -0.0500331 0.1822245 -0.275 0.783648
## Q103293.fctrNo -0.0676328 0.1544381 -0.438 0.661438
## Q103293.fctrYes 0.0061961 0.1568298 0.040 0.968485
## Q104996.fctrNo -0.0178511 0.1457711 -0.122 0.902535
## Q104996.fctrYes -0.1533878 0.1430977 -1.072 0.283761
## Q105655.fctrNo 0.2893479 0.1759471 1.645 0.100070
## Q105655.fctrYes 0.3127441 0.1730661 1.807 0.070750
## Q105840.fctrNo -0.0888897 0.1776525 -0.500 0.616824
## Q105840.fctrYes -0.1136548 0.1804288 -0.630 0.528750
## Q106042.fctrNo 0.0153972 0.1788390 0.086 0.931391
## Q106042.fctrYes -0.0960717 0.1790700 -0.537 0.591610
## Q106272.fctrNo -0.0554635 0.2037215 -0.272 0.785429
## Q106272.fctrYes 0.0682740 0.1897352 0.360 0.718968
## Q106388.fctrNo 0.0507603 0.2208328 0.230 0.818202
## Q106388.fctrYes 0.1546295 0.2349632 0.658 0.510473
## Q106389.fctrNo 0.1502679 0.2170065 0.692 0.488650
## Q106389.fctrYes 0.0293717 0.2178503 0.135 0.892750
## Q106993.fctrNo 0.3673644 0.2167993 1.694 0.090172
## Q106993.fctrYes 0.1707428 0.1904833 0.896 0.370057
## Q106997.fctrGr -0.0810373 0.1926954 -0.421 0.674087
## Q106997.fctrYy -0.3136492 0.1968576 -1.593 0.111098
## Q107491.fctrNo -0.0134033 0.1866853 -0.072 0.942764
## Q107491.fctrYes -0.0232380 0.1428647 -0.163 0.870788
## Q107869.fctrNo 0.0744925 0.1483346 0.502 0.615532
## Q107869.fctrYes 0.1210168 0.1489793 0.812 0.416616
## `Q108342.fctrIn-person` -0.0996394 0.1848678 -0.539 0.589903
## Q108342.fctrOnline -0.2754080 0.1936022 -1.423 0.154868
## Q108343.fctrNo -0.1224061 0.1876125 -0.652 0.514117
## Q108343.fctrYes -0.0067423 0.1990629 -0.034 0.972980
## Q108617.fctrNo 0.0008698 0.1695034 0.005 0.995906
## Q108617.fctrYes 0.1330221 0.2106306 0.632 0.527686
## Q108754.fctrNo 0.1507940 0.1857303 0.812 0.416850
## Q108754.fctrYes 0.1179387 0.1949446 0.605 0.545188
## Q108855.fctrUmm... 0.1631521 0.2152438 0.758 0.448458
## `Q108855.fctrYes!` 0.3086730 0.2129356 1.450 0.147168
## Q108856.fctrSocialize -0.0199749 0.2238540 -0.089 0.928898
## Q108856.fctrSpace -0.0618750 0.2085896 -0.297 0.766745
## Q108950.fctrCautious -0.2184232 0.1566298 -1.395 0.163161
## `Q108950.fctrRisk-friendly` -0.2594738 0.1702653 -1.524 0.127524
## Q109244.fctrNo 0.4876345 0.1729820 2.819 0.004818
## Q109244.fctrYes -1.1533014 0.2274689 -5.070 3.98e-07
## Q109367.fctrNo 0.0279661 0.1547476 0.181 0.856587
## Q109367.fctrYes 0.0170246 0.1468369 0.116 0.907698
## Q110740.fctrMac 0.0721362 0.1329117 0.543 0.587310
## Q110740.fctrPC 0.3088970 0.1290027 2.394 0.016643
## Q111220.fctrNo -0.1359558 0.1434797 -0.948 0.343353
## Q111220.fctrYes -0.1614926 0.1566028 -1.031 0.302436
## Q111580.fctrDemanding 0.0515213 0.1553180 0.332 0.740104
## Q111580.fctrSupportive 0.0313635 0.1446717 0.217 0.828371
## Q111848.fctrNo -0.1548796 0.1522906 -1.017 0.309153
## Q111848.fctrYes -0.2129729 0.1482870 -1.436 0.150939
## Q112270.fctrNo -0.1514147 0.1468775 -1.031 0.302592
## Q112270.fctrYes -0.0921246 0.1491233 -0.618 0.536724
## Q112478.fctrNo 0.4556014 0.1782241 2.556 0.010578
## Q112478.fctrYes 0.2814888 0.1721875 1.635 0.102095
## Q112512.fctrNo -0.2852806 0.1896960 -1.504 0.132611
## Q112512.fctrYes -0.2846300 0.1633316 -1.743 0.081395
## Q113181.fctrNo -0.0733780 0.1499333 -0.489 0.624556
## Q113181.fctrYes 0.3732298 0.1672721 2.231 0.025663
## Q113583.fctrTalk -0.0554462 0.2095860 -0.265 0.791355
## Q113583.fctrTunes -0.1530564 0.1999477 -0.765 0.443984
## Q113584.fctrPeople 0.0460928 0.2043055 0.226 0.821507
## Q113584.fctrTechnology 0.1195026 0.2026088 0.590 0.555312
## Q113992.fctrNo -0.2128343 0.1562233 -1.362 0.173081
## Q113992.fctrYes -0.2963385 0.1681820 -1.762 0.078068
## Q114152.fctrNo 0.1465889 0.1520388 0.964 0.334968
## Q114152.fctrYes 0.1029512 0.1635464 0.629 0.529027
## Q114386.fctrMysterious 0.0905564 0.1535375 0.590 0.555325
## Q114386.fctrTMI 0.1538597 0.1564976 0.983 0.325536
## Q114517.fctrNo -0.3729141 0.1704636 -2.188 0.028695
## Q114517.fctrYes -0.3290691 0.1814363 -1.814 0.069726
## Q114748.fctrNo 0.0666482 0.1827250 0.365 0.715301
## Q114748.fctrYes 0.0252582 0.1812290 0.139 0.889156
## Q114961.fctrNo 0.0542434 0.1761362 0.308 0.758111
## Q114961.fctrYes 0.0915920 0.1747754 0.524 0.600240
## Q115195.fctrNo 0.0503175 0.1669733 0.301 0.763147
## Q115195.fctrYes -0.1036019 0.1575550 -0.658 0.510821
## Q115390.fctrNo 0.0203482 0.1545509 0.132 0.895253
## Q115390.fctrYes -0.1234481 0.1439843 -0.857 0.391240
## Q115602.fctrNo -0.0592329 0.1954613 -0.303 0.761858
## Q115602.fctrYes -0.0380223 0.1750461 -0.217 0.828042
## Q115610.fctrNo 0.1261023 0.2125369 0.593 0.552967
## Q115610.fctrYes 0.0558327 0.1881532 0.297 0.766665
## Q115611.fctrNo -0.0761239 0.1982572 -0.384 0.701004
## Q115611.fctrYes 0.4475335 0.2166199 2.066 0.038830
## Q115777.fctrEnd -0.1619522 0.1663222 -0.974 0.330193
## Q115777.fctrStart -0.1527157 0.1610786 -0.948 0.343088
## Q115899.fctrCs -0.1558545 0.1664375 -0.936 0.349060
## Q115899.fctrMe 0.1185551 0.1642037 0.722 0.470295
## Q116197.fctrA.M. 0.2958096 0.1617712 1.829 0.067464
## Q116197.fctrP.M. 0.2951027 0.1503437 1.963 0.049663
## Q116441.fctrNo 0.0034699 0.1814907 0.019 0.984746
## Q116441.fctrYes -0.0365140 0.1940772 -0.188 0.850766
## Q116448.fctrNo 0.0475422 0.1733934 0.274 0.783941
## Q116448.fctrYes 0.0221346 0.1739643 0.127 0.898753
## Q116601.fctrNo -0.2809469 0.1981574 -1.418 0.156250
## Q116601.fctrYes -0.3494112 0.1696974 -2.059 0.039492
## Q116797.fctrNo 0.2786543 0.1703199 1.636 0.101826
## Q116797.fctrYes 0.2050880 0.1771501 1.158 0.246983
## Q116881.fctrHappy -0.2112954 0.1701217 -1.242 0.214227
## Q116881.fctrRight 0.0497857 0.1850385 0.269 0.787887
## Q116953.fctrNo 0.1527667 0.1801411 0.848 0.396416
## Q116953.fctrYes -0.1143126 0.1697006 -0.674 0.500557
## `Q117186.fctrCool headed` -0.0322684 0.1628647 -0.198 0.842943
## `Q117186.fctrHot headed` 0.0785754 0.1728567 0.455 0.649419
## `Q117193.fctrOdd hours` -0.0317077 0.1604626 -0.198 0.843357
## `Q117193.fctrStandard hours` 0.0632222 0.1541260 0.410 0.681661
## Q118117.fctrNo 0.0319533 0.1512148 0.211 0.832645
## Q118117.fctrYes -0.0045956 0.1529432 -0.030 0.976029
## Q118232.fctrId -0.3359695 0.1500751 -2.239 0.025177
## Q118232.fctrPr -0.2171094 0.1484404 -1.463 0.143576
## Q118233.fctrNo 0.1668660 0.1854315 0.900 0.368184
## Q118233.fctrYes -0.0228829 0.2020307 -0.113 0.909821
## Q118237.fctrNo 0.2073330 0.1874355 1.106 0.268659
## Q118237.fctrYes 0.2479353 0.1844620 1.344 0.178916
## Q118892.fctrNo -0.1793811 0.1354940 -1.324 0.185535
## Q118892.fctrYes -0.2175232 0.1282808 -1.696 0.089947
## Q119334.fctrNo 0.0919723 0.1402406 0.656 0.511941
## Q119334.fctrYes 0.0975312 0.1358965 0.718 0.472950
## Q119650.fctrGiving 0.1048846 0.1456063 0.720 0.471322
## Q119650.fctrReceiving -0.0525568 0.1624940 -0.323 0.746363
## Q119851.fctrNo 0.1384830 0.1637916 0.845 0.397841
## Q119851.fctrYes -0.0717244 0.1648165 -0.435 0.663434
## Q120012.fctrNo 0.0105537 0.1689407 0.062 0.950189
## Q120012.fctrYes -0.0604111 0.1658342 -0.364 0.715644
## Q120014.fctrNo 0.0982733 0.1547093 0.635 0.525290
## Q120014.fctrYes 0.1771983 0.1470248 1.205 0.228116
## `Q120194.fctrStudy first` -0.2164845 0.1421333 -1.523 0.127732
## `Q120194.fctrTry first` -0.1273801 0.1472460 -0.865 0.386993
## Q120379.fctrNo -0.0467041 0.1595429 -0.293 0.769723
## Q120379.fctrYes -0.2952189 0.1569848 -1.881 0.060032
## Q120472.fctrArt 0.1608545 0.1659707 0.969 0.332458
## Q120472.fctrScience 0.2806323 0.1574414 1.782 0.074675
## Q120650.fctrNo -0.1127566 0.2011217 -0.561 0.575044
## Q120650.fctrYes 0.1949465 0.1475038 1.322 0.186289
## Q120978.fctrNo -0.0705526 0.1591074 -0.443 0.657457
## Q120978.fctrYes -0.0942502 0.1572005 -0.600 0.548804
## Q121011.fctrNo -0.0791543 0.1590049 -0.498 0.618618
## Q121011.fctrYes -0.0567418 0.1569997 -0.361 0.717790
## Q121699.fctrNo -0.3592640 0.2434931 -1.475 0.140089
## Q121699.fctrYes -0.4954610 0.2369785 -2.091 0.036551
## Q121700.fctrNo 0.2982903 0.2384246 1.251 0.210902
## Q121700.fctrYes 0.2091438 0.2574265 0.812 0.416539
## Q122120.fctrNo 0.1342521 0.1395279 0.962 0.335955
## Q122120.fctrYes 0.2428534 0.1557178 1.560 0.118861
## Q122769.fctrNo -0.0461562 0.2073637 -0.223 0.823858
## Q122769.fctrYes 0.0212113 0.2113761 0.100 0.920068
## Q122770.fctrNo -0.2542574 0.2574316 -0.988 0.323314
## Q122770.fctrYes -0.1452398 0.2541540 -0.571 0.567685
## Q122771.fctrPc 0.2447209 0.2394118 1.022 0.306698
## Q122771.fctrPt 0.4752327 0.2541833 1.870 0.061533
## Q123464.fctrNo -0.0366237 0.1608730 -0.228 0.819914
## Q123464.fctrYes 0.2487526 0.2355891 1.056 0.291026
## Q123621.fctrNo 0.0984605 0.1698524 0.580 0.562129
## Q123621.fctrYes 0.1957982 0.1724035 1.136 0.256083
## Q124122.fctrNo 0.0340692 0.1408767 0.242 0.808907
## Q124122.fctrYes -0.1410493 0.1351509 -1.044 0.296651
## Q124742.fctrNo -0.2187610 0.1099528 -1.990 0.046636
## Q124742.fctrYes -0.0386472 0.1250351 -0.309 0.757252
## Q96024.fctrNo -0.1747059 0.1376644 -1.269 0.204416
## Q96024.fctrYes -0.0182186 0.1287015 -0.142 0.887430
## `Q98059.fctrOnly-child` 0.2621688 0.2584431 1.014 0.310384
## Q98059.fctrYes -0.0063685 0.2134926 -0.030 0.976203
## Q98078.fctrNo -0.1263050 0.1966296 -0.642 0.520646
## Q98078.fctrYes -0.1505191 0.1992841 -0.755 0.450069
## Q98197.fctrNo -0.5127453 0.1953448 -2.625 0.008669
## Q98197.fctrYes -0.0151156 0.2038611 -0.074 0.940894
## Q98578.fctrNo 0.3482119 0.1608310 2.165 0.030382
## Q98578.fctrYes 0.3282354 0.1689666 1.943 0.052064
## Q98869.fctrNo -0.4732979 0.1751941 -2.702 0.006901
## Q98869.fctrYes -0.2590632 0.1490595 -1.738 0.082213
## Q99480.fctrNo -0.1602427 0.2141832 -0.748 0.454366
## Q99480.fctrYes 0.1514424 0.1948876 0.777 0.437114
## Q99581.fctrNo 0.3301022 0.2111110 1.564 0.117902
## Q99581.fctrYes 0.4265928 0.2380964 1.792 0.073184
## Q99716.fctrNo -0.3276073 0.1829947 -1.790 0.073413
## Q99716.fctrYes -0.2404210 0.2319843 -1.036 0.300031
## `Q99982.fctrCheck!` 0.2645425 0.2071426 1.277 0.201566
## Q99982.fctrNope 0.1613903 0.2106029 0.766 0.443483
## YOB.Age.fctr.L -0.5079091 0.2743087 -1.852 0.064084
## YOB.Age.fctr.Q -0.5040687 0.2422157 -2.081 0.037427
## YOB.Age.fctr.C -0.1744418 0.2294627 -0.760 0.447124
## `YOB.Age.fctr^4` -0.3103575 0.2529209 -1.227 0.219788
## `YOB.Age.fctr^5` 0.1004217 0.2552820 0.393 0.694042
## `YOB.Age.fctr^6` -0.3123762 0.2277328 -1.372 0.170164
## `YOB.Age.fctr^7` -0.0517144 0.2236962 -0.231 0.817174
## `YOB.Age.fctr^8` 0.0384273 0.2383733 0.161 0.871931
## `Q109244.fctrNA:.clusterid.fctr2` 0.0324636 0.1486350 0.218 0.827108
## `Q109244.fctrNo:.clusterid.fctr2` 0.2345794 0.1696523 1.383 0.166755
## `Q109244.fctrYes:.clusterid.fctr2` -0.2148374 0.2885817 -0.744 0.456599
## `Q109244.fctrNA:.clusterid.fctr3` -0.0536347 0.1721979 -0.311 0.755442
## `Q109244.fctrNo:.clusterid.fctr3` 0.1264885 0.1619828 0.781 0.434875
## `Q109244.fctrYes:.clusterid.fctr3` -0.9580630 0.3317457 -2.888 0.003878
## `YOB.Age.fctrNA:YOB.Age.dff` NA NA NA NA
## `YOB.Age.fctr(15,20]:YOB.Age.dff` -0.1085158 0.0784371 -1.383 0.166519
## `YOB.Age.fctr(20,25]:YOB.Age.dff` 0.0221617 0.0636246 0.348 0.727600
## `YOB.Age.fctr(25,30]:YOB.Age.dff` 0.0654977 0.0721097 0.908 0.363716
## `YOB.Age.fctr(30,35]:YOB.Age.dff` -0.0203805 0.0742290 -0.275 0.783653
## `YOB.Age.fctr(35,40]:YOB.Age.dff` -0.0984378 0.0785600 -1.253 0.210196
## `YOB.Age.fctr(40,50]:YOB.Age.dff` 0.0167222 0.0323514 0.517 0.605231
## `YOB.Age.fctr(50,65]:YOB.Age.dff` -0.0062675 0.0243538 -0.257 0.796906
## `YOB.Age.fctr(65,90]:YOB.Age.dff` 0.0710993 0.0364051 1.953 0.050819
##
## (Intercept)
## .rnorm
## Edn.fctr.L
## Edn.fctr.Q
## Edn.fctr.C
## `Edn.fctr^4`
## `Edn.fctr^5`
## `Edn.fctr^6`
## `Edn.fctr^7`
## Gender.fctrF
## Gender.fctrM
## Hhold.fctrMKn
## Hhold.fctrMKy
## Hhold.fctrPKn **
## Hhold.fctrPKy
## Hhold.fctrSKn
## Hhold.fctrSKy
## Income.fctr.L
## Income.fctr.Q *
## Income.fctr.C *
## `Income.fctr^4`
## `Income.fctr^5`
## `Income.fctr^6`
## Q100010.fctrNo .
## Q100010.fctrYes .
## Q100562.fctrNo
## Q100562.fctrYes
## Q100680.fctrNo
## Q100680.fctrYes
## Q100689.fctrNo
## Q100689.fctrYes *
## Q101162.fctrOptimist
## Q101162.fctrPessimist
## Q101163.fctrDad
## Q101163.fctrMom
## Q101596.fctrNo ***
## Q101596.fctrYes ***
## Q102089.fctrOwn
## Q102089.fctrRent
## Q102289.fctrNo
## Q102289.fctrYes
## Q102674.fctrNo .
## Q102674.fctrYes
## Q102687.fctrNo .
## Q102687.fctrYes
## Q102906.fctrNo
## Q102906.fctrYes
## Q103293.fctrNo
## Q103293.fctrYes
## Q104996.fctrNo
## Q104996.fctrYes
## Q105655.fctrNo
## Q105655.fctrYes .
## Q105840.fctrNo
## Q105840.fctrYes
## Q106042.fctrNo
## Q106042.fctrYes
## Q106272.fctrNo
## Q106272.fctrYes
## Q106388.fctrNo
## Q106388.fctrYes
## Q106389.fctrNo
## Q106389.fctrYes
## Q106993.fctrNo .
## Q106993.fctrYes
## Q106997.fctrGr
## Q106997.fctrYy
## Q107491.fctrNo
## Q107491.fctrYes
## Q107869.fctrNo
## Q107869.fctrYes
## `Q108342.fctrIn-person`
## Q108342.fctrOnline
## Q108343.fctrNo
## Q108343.fctrYes
## Q108617.fctrNo
## Q108617.fctrYes
## Q108754.fctrNo
## Q108754.fctrYes
## Q108855.fctrUmm...
## `Q108855.fctrYes!`
## Q108856.fctrSocialize
## Q108856.fctrSpace
## Q108950.fctrCautious
## `Q108950.fctrRisk-friendly`
## Q109244.fctrNo **
## Q109244.fctrYes ***
## Q109367.fctrNo
## Q109367.fctrYes
## Q110740.fctrMac
## Q110740.fctrPC *
## Q111220.fctrNo
## Q111220.fctrYes
## Q111580.fctrDemanding
## Q111580.fctrSupportive
## Q111848.fctrNo
## Q111848.fctrYes
## Q112270.fctrNo
## Q112270.fctrYes
## Q112478.fctrNo *
## Q112478.fctrYes
## Q112512.fctrNo
## Q112512.fctrYes .
## Q113181.fctrNo
## Q113181.fctrYes *
## Q113583.fctrTalk
## Q113583.fctrTunes
## Q113584.fctrPeople
## Q113584.fctrTechnology
## Q113992.fctrNo
## Q113992.fctrYes .
## Q114152.fctrNo
## Q114152.fctrYes
## Q114386.fctrMysterious
## Q114386.fctrTMI
## Q114517.fctrNo *
## Q114517.fctrYes .
## Q114748.fctrNo
## Q114748.fctrYes
## Q114961.fctrNo
## Q114961.fctrYes
## Q115195.fctrNo
## Q115195.fctrYes
## Q115390.fctrNo
## Q115390.fctrYes
## Q115602.fctrNo
## Q115602.fctrYes
## Q115610.fctrNo
## Q115610.fctrYes
## Q115611.fctrNo
## Q115611.fctrYes *
## Q115777.fctrEnd
## Q115777.fctrStart
## Q115899.fctrCs
## Q115899.fctrMe
## Q116197.fctrA.M. .
## Q116197.fctrP.M. *
## Q116441.fctrNo
## Q116441.fctrYes
## Q116448.fctrNo
## Q116448.fctrYes
## Q116601.fctrNo
## Q116601.fctrYes *
## Q116797.fctrNo
## Q116797.fctrYes
## Q116881.fctrHappy
## Q116881.fctrRight
## Q116953.fctrNo
## Q116953.fctrYes
## `Q117186.fctrCool headed`
## `Q117186.fctrHot headed`
## `Q117193.fctrOdd hours`
## `Q117193.fctrStandard hours`
## Q118117.fctrNo
## Q118117.fctrYes
## Q118232.fctrId *
## Q118232.fctrPr
## Q118233.fctrNo
## Q118233.fctrYes
## Q118237.fctrNo
## Q118237.fctrYes
## Q118892.fctrNo
## Q118892.fctrYes .
## Q119334.fctrNo
## Q119334.fctrYes
## Q119650.fctrGiving
## Q119650.fctrReceiving
## Q119851.fctrNo
## Q119851.fctrYes
## Q120012.fctrNo
## Q120012.fctrYes
## Q120014.fctrNo
## Q120014.fctrYes
## `Q120194.fctrStudy first`
## `Q120194.fctrTry first`
## Q120379.fctrNo
## Q120379.fctrYes .
## Q120472.fctrArt
## Q120472.fctrScience .
## Q120650.fctrNo
## Q120650.fctrYes
## Q120978.fctrNo
## Q120978.fctrYes
## Q121011.fctrNo
## Q121011.fctrYes
## Q121699.fctrNo
## Q121699.fctrYes *
## Q121700.fctrNo
## Q121700.fctrYes
## Q122120.fctrNo
## Q122120.fctrYes
## Q122769.fctrNo
## Q122769.fctrYes
## Q122770.fctrNo
## Q122770.fctrYes
## Q122771.fctrPc
## Q122771.fctrPt .
## Q123464.fctrNo
## Q123464.fctrYes
## Q123621.fctrNo
## Q123621.fctrYes
## Q124122.fctrNo
## Q124122.fctrYes
## Q124742.fctrNo *
## Q124742.fctrYes
## Q96024.fctrNo
## Q96024.fctrYes
## `Q98059.fctrOnly-child`
## Q98059.fctrYes
## Q98078.fctrNo
## Q98078.fctrYes
## Q98197.fctrNo **
## Q98197.fctrYes
## Q98578.fctrNo *
## Q98578.fctrYes .
## Q98869.fctrNo **
## Q98869.fctrYes .
## Q99480.fctrNo
## Q99480.fctrYes
## Q99581.fctrNo
## Q99581.fctrYes .
## Q99716.fctrNo .
## Q99716.fctrYes
## `Q99982.fctrCheck!`
## Q99982.fctrNope
## YOB.Age.fctr.L .
## YOB.Age.fctr.Q *
## YOB.Age.fctr.C
## `YOB.Age.fctr^4`
## `YOB.Age.fctr^5`
## `YOB.Age.fctr^6`
## `YOB.Age.fctr^7`
## `YOB.Age.fctr^8`
## `Q109244.fctrNA:.clusterid.fctr2`
## `Q109244.fctrNo:.clusterid.fctr2`
## `Q109244.fctrYes:.clusterid.fctr2`
## `Q109244.fctrNA:.clusterid.fctr3`
## `Q109244.fctrNo:.clusterid.fctr3`
## `Q109244.fctrYes:.clusterid.fctr3` **
## `YOB.Age.fctrNA:YOB.Age.dff`
## `YOB.Age.fctr(15,20]:YOB.Age.dff`
## `YOB.Age.fctr(20,25]:YOB.Age.dff`
## `YOB.Age.fctr(25,30]:YOB.Age.dff`
## `YOB.Age.fctr(30,35]:YOB.Age.dff`
## `YOB.Age.fctr(35,40]:YOB.Age.dff`
## `YOB.Age.fctr(40,50]:YOB.Age.dff`
## `YOB.Age.fctr(50,65]:YOB.Age.dff`
## `YOB.Age.fctr(65,90]:YOB.Age.dff` .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6157.1 on 4452 degrees of freedom
## Residual deviance: 5129.5 on 4206 degrees of freedom
## AIC: 5623.5
##
## Number of Fisher Scoring iterations: 4
##
## [1] "myfit_mdl: train diagnostics complete: 17.605000 secs"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Prediction
## Reference D R
## D 1657 703
## R 665 1428
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.927914e-01 3.839985e-01 6.790059e-01 7.063236e-01 5.299798e-01
## AccuracyPValue McnemarPValue
## 1.383030e-108 3.171337e-01## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Prediction
## Reference D R
## D 490 101
## R 384 140
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.650224e-01 9.931930e-02 5.353355e-01 5.943654e-01 5.300448e-01
## AccuracyPValue McnemarPValue
## 1.034714e-02 1.538043e-37
## [1] "myfit_mdl: predict complete: 33.190000 secs"
## id
## 1 All.X##rcv#glm
## feats
## 1 Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 14.586 1.433
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6921964 0.7021186 0.6822742 0.7621422
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.6761364 0.6254219
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6790059 0.7063236 0.2481921
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5487933 0.5651438 0.5324427 0.5687249
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.65 0.3660131 0.5650224
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5353355 0.5943654 0.0993193
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01221453 0.02448413
## [1] "myfit_mdl: exit: 33.205000 secs"# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 153.497 186.758
## 5 fit.models_1_preProc 1 4 preProc 186.759 NA
## elapsed
## 4 33.261
## 5 NArequire(gdata)## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.sizemdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indepVar=indepVar, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indepVar
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indepVar_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indepVar_lst[["feat"]] <- feat
# User specified combinatorial models
# indepVar_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indepVar=indepVar,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet Q109244.fctr,Gender.fctr,Q109244.fctr:Q106272.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q98078.fctr
## Low.cor.X##rcv#glmnet Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X##rcv#glmnet Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X##rcv#glm Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr 0 0.280
## Random###myrandom_classfr 0 0.265
## Max.cor.Y.rcv.1X1###glmnet 0 0.742
## Max.cor.Y##rcv#rpart 5 1.873
## Interact.High.cor.Y##rcv#glmnet 25 5.857
## Low.cor.X##rcv#glmnet 25 23.803
## All.X##rcv#glmnet 25 22.700
## All.X##rcv#glm 1 14.586
## min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr 0.002 0.5000000
## Random###myrandom_classfr 0.002 0.4853405
## Max.cor.Y.rcv.1X1###glmnet 0.062 0.6195812
## Max.cor.Y##rcv#rpart 0.020 0.6195812
## Interact.High.cor.Y##rcv#glmnet 0.430 0.6425086
## Low.cor.X##rcv#glmnet 2.165 0.6612399
## All.X##rcv#glmnet 2.148 0.6612399
## All.X##rcv#glm 1.433 0.6921964
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 1.0000000 0.0000000 0.5000000
## Random###myrandom_classfr 0.5182203 0.4524606 0.4907312
## Max.cor.Y.rcv.1X1###glmnet 0.6720339 0.5671285 0.6728307
## Max.cor.Y##rcv#rpart 0.6720339 0.5671285 0.6630238
## Interact.High.cor.Y##rcv#glmnet 0.6309322 0.6540850 0.6985067
## Low.cor.X##rcv#glmnet 0.7080508 0.6144290 0.7306715
## All.X##rcv#glmnet 0.7080508 0.6144290 0.7306715
## All.X##rcv#glm 0.7021186 0.6822742 0.7621422
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.50 0.0000000
## Random###myrandom_classfr 0.55 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.45 0.6696922
## Max.cor.Y##rcv#rpart 0.50 0.5855945
## Interact.High.cor.Y##rcv#glmnet 0.50 0.6318948
## Low.cor.X##rcv#glmnet 0.50 0.6322517
## All.X##rcv#glmnet 0.50 0.6322517
## All.X##rcv#glm 0.50 0.6761364
## max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr 0.5299798 0.5151927
## Random###myrandom_classfr 0.5299798 0.5151927
## Max.cor.Y.rcv.1X1###glmnet 0.6240737 0.6096574
## Max.cor.Y##rcv#rpart 0.6227308 0.6083009
## Interact.High.cor.Y##rcv#glmnet 0.6250526 0.6275303
## Low.cor.X##rcv#glmnet 0.6471308 0.6499576
## All.X##rcv#glmnet 0.6471308 0.6499576
## All.X##rcv#glm 0.6254219 0.6790059
## max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr 0.5447275 0.0000000
## Random###myrandom_classfr 0.5447275 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.6383270 0.2630030
## Max.cor.Y##rcv#rpart 0.6369905 0.2400218
## Interact.High.cor.Y##rcv#glmnet 0.6559125 0.2491892
## Low.cor.X##rcv#glmnet 0.6779203 0.2907403
## All.X##rcv#glmnet 0.6779203 0.2907403
## All.X##rcv#glm 0.7063236 0.2481921
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 1.0000000 0.0000000
## Random###myrandom_classfr 0.4836608 0.5093063 0.4580153
## Max.cor.Y.rcv.1X1###glmnet 0.4999322 0.5532995 0.4465649
## Max.cor.Y##rcv#rpart 0.4999322 0.5532995 0.4465649
## Interact.High.cor.Y##rcv#glmnet 0.5218093 0.5245347 0.5190840
## Low.cor.X##rcv#glmnet 0.5588180 0.6023689 0.5152672
## All.X##rcv#glmnet 0.5588180 0.6023689 0.5152672
## All.X##rcv#glm 0.5487933 0.5651438 0.5324427
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.50
## Random###myrandom_classfr 0.5181895 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.5102459 0.65
## Max.cor.Y##rcv#rpart 0.5000646 0.65
## Interact.High.cor.Y##rcv#glmnet 0.5242069 0.70
## Low.cor.X##rcv#glmnet 0.5757966 0.55
## All.X##rcv#glmnet 0.5757966 0.55
## All.X##rcv#glm 0.5687249 0.65
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.00000000 0.5300448
## Random###myrandom_classfr 0.00000000 0.5300448
## Max.cor.Y.rcv.1X1###glmnet 0.00000000 0.5300448
## Max.cor.Y##rcv#rpart 0.00000000 0.5300448
## Interact.High.cor.Y##rcv#glmnet 0.08465608 0.5345291
## Low.cor.X##rcv#glmnet 0.46241458 0.5766816
## All.X##rcv#glmnet 0.46241458 0.5766816
## All.X##rcv#glm 0.36601307 0.5650224
## max.AccuracyLower.OOB
## MFO###myMFO_classfr 0.5002547
## Random###myrandom_classfr 0.5002547
## Max.cor.Y.rcv.1X1###glmnet 0.5002547
## Max.cor.Y##rcv#rpart 0.5002547
## Interact.High.cor.Y##rcv#glmnet 0.5047442
## Low.cor.X##rcv#glmnet 0.5470616
## All.X##rcv#glmnet 0.5470616
## All.X##rcv#glm 0.5353355
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr 0.5596760 0.00000000
## Random###myrandom_classfr 0.5596760 0.00000000
## Max.cor.Y.rcv.1X1###glmnet 0.5596760 0.00000000
## Max.cor.Y##rcv#rpart 0.5596760 0.00000000
## Interact.High.cor.Y##rcv#glmnet 0.5641315 0.01440199
## Low.cor.X##rcv#glmnet 0.6058959 0.13437657
## All.X##rcv#glmnet 0.6058959 0.13437657
## All.X##rcv#glm 0.5943654 0.09931930
## max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr NA NA
## Random###myrandom_classfr NA NA
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.01116620 0.02269692
## Interact.High.cor.Y##rcv#glmnet 0.01599098 0.03290196
## Low.cor.X##rcv#glmnet 0.00812887 0.01734997
## All.X##rcv#glmnet 0.00812887 0.01734997
## All.X##rcv#glm 0.01221453 0.02448413rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 186.759 188.689
## 6 fit.models_1_end 1 5 teardown 188.689 NA
## elapsed
## 5 1.93
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 3 fit.models 2 1 1 108.993 188.699 79.706
## 4 fit.models 2 2 2 188.699 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 192.861 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet Q109244.fctr,Gender.fctr,Q109244.fctr:Q106272.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q98078.fctr
## Low.cor.X##rcv#glmnet Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X##rcv#glmnet Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X##rcv#glm Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr 0 0.5000000
## Random###myrandom_classfr 0 0.4853405
## Max.cor.Y.rcv.1X1###glmnet 0 0.6195812
## Max.cor.Y##rcv#rpart 5 0.6195812
## Interact.High.cor.Y##rcv#glmnet 25 0.6425086
## Low.cor.X##rcv#glmnet 25 0.6612399
## All.X##rcv#glmnet 25 0.6612399
## All.X##rcv#glm 1 0.6921964
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 1.0000000 0.0000000 0.5000000
## Random###myrandom_classfr 0.5182203 0.4524606 0.4907312
## Max.cor.Y.rcv.1X1###glmnet 0.6720339 0.5671285 0.6728307
## Max.cor.Y##rcv#rpart 0.6720339 0.5671285 0.6630238
## Interact.High.cor.Y##rcv#glmnet 0.6309322 0.6540850 0.6985067
## Low.cor.X##rcv#glmnet 0.7080508 0.6144290 0.7306715
## All.X##rcv#glmnet 0.7080508 0.6144290 0.7306715
## All.X##rcv#glm 0.7021186 0.6822742 0.7621422
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.50 0.0000000
## Random###myrandom_classfr 0.55 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.45 0.6696922
## Max.cor.Y##rcv#rpart 0.50 0.5855945
## Interact.High.cor.Y##rcv#glmnet 0.50 0.6318948
## Low.cor.X##rcv#glmnet 0.50 0.6322517
## All.X##rcv#glmnet 0.50 0.6322517
## All.X##rcv#glm 0.50 0.6761364
## max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr 0.5299798 0.0000000
## Random###myrandom_classfr 0.5299798 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.6240737 0.2630030
## Max.cor.Y##rcv#rpart 0.6227308 0.2400218
## Interact.High.cor.Y##rcv#glmnet 0.6250526 0.2491892
## Low.cor.X##rcv#glmnet 0.6471308 0.2907403
## All.X##rcv#glmnet 0.6471308 0.2907403
## All.X##rcv#glm 0.6254219 0.2481921
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 1.0000000 0.0000000
## Random###myrandom_classfr 0.4836608 0.5093063 0.4580153
## Max.cor.Y.rcv.1X1###glmnet 0.4999322 0.5532995 0.4465649
## Max.cor.Y##rcv#rpart 0.4999322 0.5532995 0.4465649
## Interact.High.cor.Y##rcv#glmnet 0.5218093 0.5245347 0.5190840
## Low.cor.X##rcv#glmnet 0.5588180 0.6023689 0.5152672
## All.X##rcv#glmnet 0.5588180 0.6023689 0.5152672
## All.X##rcv#glm 0.5487933 0.5651438 0.5324427
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.50
## Random###myrandom_classfr 0.5181895 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.5102459 0.65
## Max.cor.Y##rcv#rpart 0.5000646 0.65
## Interact.High.cor.Y##rcv#glmnet 0.5242069 0.70
## Low.cor.X##rcv#glmnet 0.5757966 0.55
## All.X##rcv#glmnet 0.5757966 0.55
## All.X##rcv#glm 0.5687249 0.65
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.00000000 0.5300448
## Random###myrandom_classfr 0.00000000 0.5300448
## Max.cor.Y.rcv.1X1###glmnet 0.00000000 0.5300448
## Max.cor.Y##rcv#rpart 0.00000000 0.5300448
## Interact.High.cor.Y##rcv#glmnet 0.08465608 0.5345291
## Low.cor.X##rcv#glmnet 0.46241458 0.5766816
## All.X##rcv#glmnet 0.46241458 0.5766816
## All.X##rcv#glm 0.36601307 0.5650224
## max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr 0.00000000 3.57142857
## Random###myrandom_classfr 0.00000000 3.77358491
## Max.cor.Y.rcv.1X1###glmnet 0.00000000 1.34770889
## Max.cor.Y##rcv#rpart 0.00000000 0.53390283
## Interact.High.cor.Y##rcv#glmnet 0.01440199 0.17073587
## Low.cor.X##rcv#glmnet 0.13437657 0.04201151
## All.X##rcv#glmnet 0.13437657 0.04405286
## All.X##rcv#glm 0.09931930 0.06855889
## inv.elapsedtime.final
## MFO###myMFO_classfr 500.0000000
## Random###myrandom_classfr 500.0000000
## Max.cor.Y.rcv.1X1###glmnet 16.1290323
## Max.cor.Y##rcv#rpart 50.0000000
## Interact.High.cor.Y##rcv#glmnet 2.3255814
## Low.cor.X##rcv#glmnet 0.4618938
## All.X##rcv#glmnet 0.4655493
## All.X##rcv#glm 0.6978367# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id max.Accuracy.OOB max.AUCROCR.OOB
## 6 Low.cor.X##rcv#glmnet 0.5766816 0.5757966
## 7 All.X##rcv#glmnet 0.5766816 0.5757966
## 8 All.X##rcv#glm 0.5650224 0.5687249
## 5 Interact.High.cor.Y##rcv#glmnet 0.5345291 0.5242069
## 2 Random###myrandom_classfr 0.5300448 0.5181895
## 3 Max.cor.Y.rcv.1X1###glmnet 0.5300448 0.5102459
## 4 Max.cor.Y##rcv#rpart 0.5300448 0.5000646
## 1 MFO###myMFO_classfr 0.5300448 0.5000000
## max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 6 0.5588180 0.6471308 0.50
## 7 0.5588180 0.6471308 0.50
## 8 0.5487933 0.6254219 0.50
## 5 0.5218093 0.6250526 0.50
## 2 0.4836608 0.5299798 0.55
## 3 0.4999322 0.6240737 0.45
## 4 0.4999322 0.6227308 0.50
## 1 0.5000000 0.5299798 0.50
## opt.prob.threshold.OOB
## 6 0.55
## 7 0.55
## 8 0.65
## 5 0.70
## 2 0.55
## 3 0.65
## 4 0.65
## 1 0.50# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7f7fc1933190>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Low.cor.X##rcv#glmnet"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glbMdlSelId))
glbMdlSelId <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glbMdlSelId)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])
## Length Class Mode
## a0 84 -none- numeric
## beta 20748 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 247 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.231235208 0.105996862
## Hhold.fctrMKy Hhold.fctrPKn
## 0.037530839 -0.462338411
## Income.fctr.Q Income.fctr.C
## 0.050511902 0.018998965
## Q100689.fctrYes Q101163.fctrDad
## -0.055283639 0.146113017
## Q101596.fctrYes Q102089.fctrRent
## 0.036618639 -0.015340306
## Q106042.fctrNo Q106272.fctrYes
## 0.011463411 0.009424122
## Q106388.fctrYes Q106997.fctrGr
## 0.026714803 0.075133557
## Q108855.fctrYes! Q109244.fctrNo
## 0.029034122 0.424732820
## Q109244.fctrYes Q110740.fctrPC
## -1.290091615 0.083252496
## Q112478.fctrNo Q113181.fctrNo
## 0.010843412 -0.088149365
## Q113181.fctrYes Q115195.fctrYes
## 0.187631356 -0.026839083
## Q115611.fctrNo Q115611.fctrYes
## -0.142804586 0.238183824
## Q115899.fctrCs Q116881.fctrHappy
## -0.090071561 -0.008298821
## Q116881.fctrRight Q116953.fctrNo
## 0.093162156 0.028952204
## Q118232.fctrId Q119851.fctrNo
## -0.016361288 0.100215288
## Q120379.fctrYes Q120472.fctrScience
## -0.104208571 0.057685089
## Q120650.fctrYes Q122771.fctrPt
## 0.046857175 0.073870604
## Q96024.fctrNo Q98197.fctrNo
## -0.015142098 -0.317900131
## Q98197.fctrYes Q98869.fctrNo
## 0.019623012 -0.054425151
## Q99480.fctrNo Q109244.fctrYes:.clusterid.fctr3
## -0.060237559 -0.340923455
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.013105017
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrM
## -0.2330609616 0.1054981329
## Hhold.fctrMKy Hhold.fctrPKn
## 0.0441815228 -0.4832355082
## Income.fctr.Q Income.fctr.C
## 0.0602852373 0.0347677155
## Q100689.fctrYes Q101163.fctrDad
## -0.0683272189 0.1533257769
## Q101596.fctrYes Q102089.fctrRent
## 0.0439390076 -0.0239022190
## Q104996.fctrNo Q106042.fctrNo
## 0.0043630800 0.0181892131
## Q106272.fctrYes Q106388.fctrYes
## 0.0133561866 0.0322154731
## Q106389.fctrNo Q106997.fctrGr
## 0.0034398840 0.0883487716
## Q108342.fctrOnline Q108855.fctrYes!
## -0.0024951688 0.0384230419
## Q109244.fctrNo Q109244.fctrYes
## 0.4252674971 -1.2944252294
## Q110740.fctrPC Q112478.fctrNo
## 0.0904838371 0.0206308499
## Q113181.fctrNo Q113181.fctrYes
## -0.0974065367 0.1868282743
## Q113583.fctrTunes Q115195.fctrYes
## -0.0004141873 -0.0375439703
## Q115611.fctrNo Q115611.fctrYes
## -0.1393412913 0.2431770094
## Q115899.fctrCs Q116881.fctrHappy
## -0.1011804925 -0.0184386866
## Q116881.fctrRight Q116953.fctrNo
## 0.0961084981 0.0426058122
## Q118232.fctrId Q119851.fctrNo
## -0.0266978568 0.1074911511
## Q119851.fctrYes Q120379.fctrYes
## -0.0033132601 -0.1185959917
## Q120472.fctrScience Q120650.fctrYes
## 0.0652028941 0.0591120125
## Q121699.fctrYes Q122120.fctrYes
## -0.0080339783 0.0008435008
## Q122771.fctrPt Q124742.fctrNo
## 0.0876713081 -0.0112811758
## Q96024.fctrNo Q98197.fctrNo
## -0.0246782767 -0.3250116396
## Q98197.fctrYes Q98869.fctrNo
## 0.0194484440 -0.0664120602
## Q99480.fctrNo Q99480.fctrYes
## -0.0685669208 0.0052925176
## Q109244.fctrYes:.clusterid.fctr3 YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.3783124405 -0.0181184935## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp
## Q109244.fctrYes 1.000000e+02 1.000000e+02
## Hhold.fctrPKn 3.628540e+01 3.628540e+01
## Q109244.fctrNo 3.290204e+01 3.290204e+01
## Q109244.fctrYes:.clusterid.fctr3 2.726523e+01 2.726523e+01
## Q98197.fctrNo 2.478156e+01 2.478156e+01
## Q115611.fctrYes 1.855961e+01 1.855961e+01
## Q113181.fctrYes 1.451086e+01 1.451086e+01
## Q101163.fctrDad 1.148138e+01 1.148138e+01
## Q115611.fctrNo 1.097807e+01 1.097807e+01
## Q120379.fctrYes 8.402531e+00 8.402531e+00
## Gender.fctrM 8.196442e+00 8.196442e+00
## Q119851.fctrNo 7.928697e+00 7.928697e+00
## Q116881.fctrRight 7.282315e+00 7.282315e+00
## Q115899.fctrCs 7.231929e+00 7.231929e+00
## Q113181.fctrNo 7.040219e+00 7.040219e+00
## Q110740.fctrPC 6.614133e+00 6.614133e+00
## Q106997.fctrGr 6.123942e+00 6.123942e+00
## Q122771.fctrPt 6.039696e+00 6.039696e+00
## Q99480.fctrNo 4.857361e+00 4.857361e+00
## Q120472.fctrScience 4.640923e+00 4.640923e+00
## Q100689.fctrYes 4.582868e+00 4.582868e+00
## Q98869.fctrNo 4.491932e+00 4.491932e+00
## Income.fctr.Q 4.137668e+00 4.137668e+00
## Q120650.fctrYes 3.912099e+00 3.912099e+00
## Hhold.fctrMKy 3.060185e+00 3.060185e+00
## Q101596.fctrYes 3.005049e+00 3.005049e+00
## Q116953.fctrNo 2.557984e+00 2.557984e+00
## Q108855.fctrYes! 2.465614e+00 2.465614e+00
## Q115195.fctrYes 2.326099e+00 2.326099e+00
## Q106388.fctrYes 2.196014e+00 2.196014e+00
## Income.fctr.C 1.836203e+00 1.836203e+00
## Q98197.fctrYes 1.515489e+00 1.515489e+00
## Q118232.fctrId 1.506214e+00 1.506214e+00
## Q96024.fctrNo 1.393278e+00 1.393278e+00
## Q102089.fctrRent 1.386075e+00 1.386075e+00
## YOB.Age.fctr(35,40]:YOB.Age.dff 1.130848e+00 1.130848e+00
## Q112478.fctrNo 1.066220e+00 1.066220e+00
## Q106042.fctrNo 1.043363e+00 1.043363e+00
## Q116881.fctrHappy 8.773345e-01 8.773345e-01
## Q106272.fctrYes 8.207824e-01 8.207824e-01
## Q124742.fctrNo 2.611237e-01 2.611237e-01
## Q121699.fctrYes 1.859613e-01 1.859613e-01
## Q99480.fctrYes 1.225051e-01 1.225051e-01
## Q104996.fctrNo 1.009916e-01 1.009916e-01
## Q106389.fctrNo 7.962249e-02 7.962249e-02
## Q119851.fctrYes 7.669155e-02 7.669155e-02
## Q108342.fctrOnline 5.775531e-02 5.775531e-02
## Q122120.fctrYes 1.952439e-02 1.952439e-02
## Q113583.fctrTunes 9.587133e-03 9.587133e-03
## .rnorm 0.000000e+00 0.000000e+00
## Edn.fctr.L 0.000000e+00 0.000000e+00
## Edn.fctr.Q 0.000000e+00 0.000000e+00
## Edn.fctr.C 0.000000e+00 0.000000e+00
## Edn.fctr^4 0.000000e+00 0.000000e+00
## Edn.fctr^5 0.000000e+00 0.000000e+00
## Edn.fctr^6 0.000000e+00 0.000000e+00
## Edn.fctr^7 0.000000e+00 0.000000e+00
## Gender.fctrF 0.000000e+00 0.000000e+00
## Hhold.fctrMKn 0.000000e+00 0.000000e+00
## Hhold.fctrPKy 0.000000e+00 0.000000e+00
## Hhold.fctrSKn 0.000000e+00 0.000000e+00
## Hhold.fctrSKy 0.000000e+00 0.000000e+00
## Income.fctr.L 0.000000e+00 0.000000e+00
## Income.fctr^4 0.000000e+00 0.000000e+00
## Income.fctr^5 0.000000e+00 0.000000e+00
## Income.fctr^6 0.000000e+00 0.000000e+00
## Q100010.fctrNo 0.000000e+00 0.000000e+00
## Q100010.fctrYes 0.000000e+00 0.000000e+00
## Q100562.fctrNo 0.000000e+00 0.000000e+00
## Q100562.fctrYes 0.000000e+00 0.000000e+00
## Q100680.fctrNo 0.000000e+00 0.000000e+00
## Q100680.fctrYes 0.000000e+00 0.000000e+00
## Q100689.fctrNo 0.000000e+00 0.000000e+00
## Q101162.fctrOptimist 0.000000e+00 0.000000e+00
## Q101162.fctrPessimist 0.000000e+00 0.000000e+00
## Q101163.fctrMom 0.000000e+00 0.000000e+00
## Q101596.fctrNo 0.000000e+00 0.000000e+00
## Q102089.fctrOwn 0.000000e+00 0.000000e+00
## Q102289.fctrNo 0.000000e+00 0.000000e+00
## Q102289.fctrYes 0.000000e+00 0.000000e+00
## Q102674.fctrNo 0.000000e+00 0.000000e+00
## Q102674.fctrYes 0.000000e+00 0.000000e+00
## Q102687.fctrNo 0.000000e+00 0.000000e+00
## Q102687.fctrYes 0.000000e+00 0.000000e+00
## Q102906.fctrNo 0.000000e+00 0.000000e+00
## Q102906.fctrYes 0.000000e+00 0.000000e+00
## Q103293.fctrNo 0.000000e+00 0.000000e+00
## Q103293.fctrYes 0.000000e+00 0.000000e+00
## Q104996.fctrYes 0.000000e+00 0.000000e+00
## Q105655.fctrNo 0.000000e+00 0.000000e+00
## Q105655.fctrYes 0.000000e+00 0.000000e+00
## Q105840.fctrNo 0.000000e+00 0.000000e+00
## Q105840.fctrYes 0.000000e+00 0.000000e+00
## Q106042.fctrYes 0.000000e+00 0.000000e+00
## Q106272.fctrNo 0.000000e+00 0.000000e+00
## Q106388.fctrNo 0.000000e+00 0.000000e+00
## Q106389.fctrYes 0.000000e+00 0.000000e+00
## Q106993.fctrNo 0.000000e+00 0.000000e+00
## Q106993.fctrYes 0.000000e+00 0.000000e+00
## Q106997.fctrYy 0.000000e+00 0.000000e+00
## Q107491.fctrNo 0.000000e+00 0.000000e+00
## Q107491.fctrYes 0.000000e+00 0.000000e+00
## Q107869.fctrNo 0.000000e+00 0.000000e+00
## Q107869.fctrYes 0.000000e+00 0.000000e+00
## Q108342.fctrIn-person 0.000000e+00 0.000000e+00
## Q108343.fctrNo 0.000000e+00 0.000000e+00
## Q108343.fctrYes 0.000000e+00 0.000000e+00
## Q108617.fctrNo 0.000000e+00 0.000000e+00
## Q108617.fctrYes 0.000000e+00 0.000000e+00
## Q108754.fctrNo 0.000000e+00 0.000000e+00
## Q108754.fctrYes 0.000000e+00 0.000000e+00
## Q108855.fctrUmm... 0.000000e+00 0.000000e+00
## Q108856.fctrSocialize 0.000000e+00 0.000000e+00
## Q108856.fctrSpace 0.000000e+00 0.000000e+00
## Q108950.fctrCautious 0.000000e+00 0.000000e+00
## Q108950.fctrRisk-friendly 0.000000e+00 0.000000e+00
## Q109367.fctrNo 0.000000e+00 0.000000e+00
## Q109367.fctrYes 0.000000e+00 0.000000e+00
## Q110740.fctrMac 0.000000e+00 0.000000e+00
## Q111220.fctrNo 0.000000e+00 0.000000e+00
## Q111220.fctrYes 0.000000e+00 0.000000e+00
## Q111580.fctrDemanding 0.000000e+00 0.000000e+00
## Q111580.fctrSupportive 0.000000e+00 0.000000e+00
## Q111848.fctrNo 0.000000e+00 0.000000e+00
## Q111848.fctrYes 0.000000e+00 0.000000e+00
## Q112270.fctrNo 0.000000e+00 0.000000e+00
## Q112270.fctrYes 0.000000e+00 0.000000e+00
## Q112478.fctrYes 0.000000e+00 0.000000e+00
## Q112512.fctrNo 0.000000e+00 0.000000e+00
## Q112512.fctrYes 0.000000e+00 0.000000e+00
## Q113583.fctrTalk 0.000000e+00 0.000000e+00
## Q113584.fctrPeople 0.000000e+00 0.000000e+00
## Q113584.fctrTechnology 0.000000e+00 0.000000e+00
## Q113992.fctrNo 0.000000e+00 0.000000e+00
## Q113992.fctrYes 0.000000e+00 0.000000e+00
## Q114152.fctrNo 0.000000e+00 0.000000e+00
## Q114152.fctrYes 0.000000e+00 0.000000e+00
## Q114386.fctrMysterious 0.000000e+00 0.000000e+00
## Q114386.fctrTMI 0.000000e+00 0.000000e+00
## Q114517.fctrNo 0.000000e+00 0.000000e+00
## Q114517.fctrYes 0.000000e+00 0.000000e+00
## Q114748.fctrNo 0.000000e+00 0.000000e+00
## Q114748.fctrYes 0.000000e+00 0.000000e+00
## Q114961.fctrNo 0.000000e+00 0.000000e+00
## Q114961.fctrYes 0.000000e+00 0.000000e+00
## Q115195.fctrNo 0.000000e+00 0.000000e+00
## Q115390.fctrNo 0.000000e+00 0.000000e+00
## Q115390.fctrYes 0.000000e+00 0.000000e+00
## Q115602.fctrNo 0.000000e+00 0.000000e+00
## Q115602.fctrYes 0.000000e+00 0.000000e+00
## Q115610.fctrNo 0.000000e+00 0.000000e+00
## Q115610.fctrYes 0.000000e+00 0.000000e+00
## Q115777.fctrEnd 0.000000e+00 0.000000e+00
## Q115777.fctrStart 0.000000e+00 0.000000e+00
## Q115899.fctrMe 0.000000e+00 0.000000e+00
## Q116197.fctrA.M. 0.000000e+00 0.000000e+00
## Q116197.fctrP.M. 0.000000e+00 0.000000e+00
## Q116441.fctrNo 0.000000e+00 0.000000e+00
## Q116441.fctrYes 0.000000e+00 0.000000e+00
## Q116448.fctrNo 0.000000e+00 0.000000e+00
## Q116448.fctrYes 0.000000e+00 0.000000e+00
## Q116601.fctrNo 0.000000e+00 0.000000e+00
## Q116601.fctrYes 0.000000e+00 0.000000e+00
## Q116797.fctrNo 0.000000e+00 0.000000e+00
## Q116797.fctrYes 0.000000e+00 0.000000e+00
## Q116953.fctrYes 0.000000e+00 0.000000e+00
## Q117186.fctrCool headed 0.000000e+00 0.000000e+00
## Q117186.fctrHot headed 0.000000e+00 0.000000e+00
## Q117193.fctrOdd hours 0.000000e+00 0.000000e+00
## Q117193.fctrStandard hours 0.000000e+00 0.000000e+00
## Q118117.fctrNo 0.000000e+00 0.000000e+00
## Q118117.fctrYes 0.000000e+00 0.000000e+00
## Q118232.fctrPr 0.000000e+00 0.000000e+00
## Q118233.fctrNo 0.000000e+00 0.000000e+00
## Q118233.fctrYes 0.000000e+00 0.000000e+00
## Q118237.fctrNo 0.000000e+00 0.000000e+00
## Q118237.fctrYes 0.000000e+00 0.000000e+00
## Q118892.fctrNo 0.000000e+00 0.000000e+00
## Q118892.fctrYes 0.000000e+00 0.000000e+00
## Q119334.fctrNo 0.000000e+00 0.000000e+00
## Q119334.fctrYes 0.000000e+00 0.000000e+00
## Q119650.fctrGiving 0.000000e+00 0.000000e+00
## Q119650.fctrReceiving 0.000000e+00 0.000000e+00
## Q120012.fctrNo 0.000000e+00 0.000000e+00
## Q120012.fctrYes 0.000000e+00 0.000000e+00
## Q120014.fctrNo 0.000000e+00 0.000000e+00
## Q120014.fctrYes 0.000000e+00 0.000000e+00
## Q120194.fctrStudy first 0.000000e+00 0.000000e+00
## Q120194.fctrTry first 0.000000e+00 0.000000e+00
## Q120379.fctrNo 0.000000e+00 0.000000e+00
## Q120472.fctrArt 0.000000e+00 0.000000e+00
## Q120650.fctrNo 0.000000e+00 0.000000e+00
## Q120978.fctrNo 0.000000e+00 0.000000e+00
## Q120978.fctrYes 0.000000e+00 0.000000e+00
## Q121011.fctrNo 0.000000e+00 0.000000e+00
## Q121011.fctrYes 0.000000e+00 0.000000e+00
## Q121699.fctrNo 0.000000e+00 0.000000e+00
## Q121700.fctrNo 0.000000e+00 0.000000e+00
## Q121700.fctrYes 0.000000e+00 0.000000e+00
## Q122120.fctrNo 0.000000e+00 0.000000e+00
## Q122769.fctrNo 0.000000e+00 0.000000e+00
## Q122769.fctrYes 0.000000e+00 0.000000e+00
## Q122770.fctrNo 0.000000e+00 0.000000e+00
## Q122770.fctrYes 0.000000e+00 0.000000e+00
## Q122771.fctrPc 0.000000e+00 0.000000e+00
## Q123464.fctrNo 0.000000e+00 0.000000e+00
## Q123464.fctrYes 0.000000e+00 0.000000e+00
## Q123621.fctrNo 0.000000e+00 0.000000e+00
## Q123621.fctrYes 0.000000e+00 0.000000e+00
## Q124122.fctrNo 0.000000e+00 0.000000e+00
## Q124122.fctrYes 0.000000e+00 0.000000e+00
## Q124742.fctrYes 0.000000e+00 0.000000e+00
## Q96024.fctrYes 0.000000e+00 0.000000e+00
## Q98059.fctrOnly-child 0.000000e+00 0.000000e+00
## Q98059.fctrYes 0.000000e+00 0.000000e+00
## Q98078.fctrNo 0.000000e+00 0.000000e+00
## Q98078.fctrYes 0.000000e+00 0.000000e+00
## Q98578.fctrNo 0.000000e+00 0.000000e+00
## Q98578.fctrYes 0.000000e+00 0.000000e+00
## Q98869.fctrYes 0.000000e+00 0.000000e+00
## Q99581.fctrNo 0.000000e+00 0.000000e+00
## Q99581.fctrYes 0.000000e+00 0.000000e+00
## Q99716.fctrNo 0.000000e+00 0.000000e+00
## Q99716.fctrYes 0.000000e+00 0.000000e+00
## Q99982.fctrCheck! 0.000000e+00 0.000000e+00
## Q99982.fctrNope 0.000000e+00 0.000000e+00
## YOB.Age.fctr.L 0.000000e+00 0.000000e+00
## YOB.Age.fctr.Q 0.000000e+00 0.000000e+00
## YOB.Age.fctr.C 0.000000e+00 0.000000e+00
## YOB.Age.fctr^4 0.000000e+00 0.000000e+00
## YOB.Age.fctr^5 0.000000e+00 0.000000e+00
## YOB.Age.fctr^6 0.000000e+00 0.000000e+00
## YOB.Age.fctr^7 0.000000e+00 0.000000e+00
## YOB.Age.fctr^8 0.000000e+00 0.000000e+00
## Q109244.fctrNA:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Q109244.fctrYes:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Q109244.fctrNA:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr3 0.000000e+00 0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.000000e+00 0.000000e+00#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
require(lazyeval)
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 109
## Loading required package: lazyeval
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 3895 R 0.06723964
## 2 2749 R 0.08013563
## 3 1236 R 0.08397483
## 4 1515 R 0.10011683
## 5 468 R 0.10237246
## 6 1307 R 0.10341420
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.9327604 FALSE
## 2 0.9198644 FALSE
## 3 0.9160252 FALSE
## 4 0.8998832 FALSE
## 5 0.8976275 FALSE
## 6 0.8965858 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.4827604
## 2 FALSE -0.4698644
## 3 FALSE -0.4660252
## 4 FALSE -0.4498832
## 5 FALSE -0.4476275
## 6 FALSE -0.4465858
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 107 4737 R 0.4038316
## 162 6665 R 0.4567505
## 224 6255 R 0.4830840
## 271 679 R 0.5088621
## 447 5858 D 0.7081985
## 457 2020 D 0.7244116
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 107 D TRUE
## 162 D TRUE
## 224 D TRUE
## 271 D TRUE
## 447 R TRUE
## 457 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 107 0.5961684
## 162 0.5432495
## 224 0.5169160
## 271 0.4911379
## 447 0.7081985
## 457 0.7244116
## Party.fctr.All.X..rcv.glmnet.is.acc
## 107 FALSE
## 162 FALSE
## 224 FALSE
## 271 FALSE
## 447 FALSE
## 457 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 107 FALSE
## 162 FALSE
## 224 FALSE
## 271 FALSE
## 447 FALSE
## 457 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 107 -0.14616842
## 162 -0.09324951
## 224 -0.06691600
## 271 -0.04113792
## 447 0.15819853
## 457 0.17441155
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 467 3978 D 0.7632908
## 468 892 D 0.7639110
## 469 5544 D 0.7722932
## 470 3006 D 0.7821974
## 471 1309 D 0.7929111
## 472 217 D 0.8063896
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 467 R TRUE
## 468 R TRUE
## 469 R TRUE
## 470 R TRUE
## 471 R TRUE
## 472 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 467 0.7632908
## 468 0.7639110
## 469 0.7722932
## 470 0.7821974
## 471 0.7929111
## 472 0.8063896
## Party.fctr.All.X..rcv.glmnet.is.acc
## 467 FALSE
## 468 FALSE
## 469 FALSE
## 470 FALSE
## 471 FALSE
## 472 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 467 FALSE
## 468 FALSE
## 469 FALSE
## 470 FALSE
## 471 FALSE
## 472 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 467 0.2132908
## 468 0.2139110
## 469 0.2222932
## 470 0.2321974
## 471 0.2429111
## 472 0.2563896
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## Q109244.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## No No 498 1961 622 0.4403773 0.4466368
## NA NA 438 1746 547 0.3920952 0.3928251
## Yes Yes 179 746 223 0.1675275 0.1605381
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## No 0.4468391 893.0164 0.4553883 1961 241.6305
## NA 0.3929598 840.5403 0.4814091 1746 211.2200
## Yes 0.1602011 182.7880 0.2450242 746 84.8165
## err.abs.OOB.mean
## No 0.4852018
## NA 0.4822375
## Yes 0.4738352
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1115.000000 4453.000000 1392.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 1916.344770 1.181822
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 4453.000000 537.667045 1.441275write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 201.636 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 4 fit.models 2 2 2 188.699 201.647 12.948
## 5 fit.models 2 3 3 201.648 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 5 fit.models 2 3 3 201.648 206.51
## 6 fit.data.training 3 0 0 206.511 NA
## elapsed
## 5 4.863
## 6 NAStep 3.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glbMdlSelId")
glbMdlFinId <- paste0("Final.", glbMdlSelId)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
mdlDf$id <- glbMdlFinId
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indepVar <- mygetIndepVar(glb_feats_df)
rfe_trn_results <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glbMdlSelId
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glbMdlSelId)) != -1))
ths_preProcess <- str_sub(glbMdlSelId, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
"Final.Ensemble", "Final")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.681000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0113 on full training set
## [1] "myfit_mdl: train complete: 27.756000 secs"
## Length Class Mode
## a0 77 -none- numeric
## beta 19019 dgCMatrix S4
## df 77 -none- numeric
## dim 2 -none- numeric
## lambda 77 -none- numeric
## dev.ratio 77 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 247 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.2137450170 -0.0137298532
## Gender.fctrM Hhold.fctrMKy
## 0.1118018113 0.0915613859
## Hhold.fctrPKn Income.fctr.Q
## -0.3784350463 0.0526235541
## Income.fctr.C Q100689.fctrYes
## 0.0650320100 -0.0736391312
## Q101163.fctrDad Q101163.fctrMom
## 0.0897328022 -0.0563376077
## Q104996.fctrNo Q106042.fctrNo
## 0.0256684069 0.0276582966
## Q106389.fctrNo Q106997.fctrGr
## 0.0218183330 0.0741399988
## Q108855.fctrYes! Q109244.fctrNo
## 0.0264495567 0.3451969230
## Q109244.fctrYes Q110740.fctrMac
## -0.8392352985 -0.0257825650
## Q110740.fctrPC Q112478.fctrNo
## 0.0706111255 0.0365651586
## Q113181.fctrNo Q113181.fctrYes
## -0.1151975866 0.1720440647
## Q115195.fctrYes Q115390.fctrNo
## -0.0158094478 0.0068169219
## Q115390.fctrYes Q115611.fctrNo
## -0.0528295854 -0.1233487967
## Q115611.fctrYes Q115899.fctrCs
## 0.3335465226 -0.0511448388
## Q116881.fctrHappy Q116881.fctrRight
## -0.0144787609 0.1668155686
## Q116953.fctrNo Q118232.fctrId
## 0.0310715279 -0.0950495921
## Q118233.fctrNo Q119851.fctrNo
## 0.0004790798 0.1026921947
## Q120194.fctrStudy first Q120379.fctrNo
## -0.0404780724 0.0101991233
## Q120379.fctrYes Q120472.fctrScience
## -0.0870614251 0.0842473016
## Q120650.fctrYes Q121699.fctrYes
## 0.0006584151 -0.0293070511
## Q122120.fctrYes Q122771.fctrPt
## 0.0132816557 0.0802288638
## Q124742.fctrNo Q98197.fctrNo
## -0.0055662028 -0.2866154026
## Q98197.fctrYes Q98869.fctrNo
## 0.0193407743 -0.1879800673
## Q99480.fctrNo Q99480.fctrYes
## -0.0342632866 0.0493911301
## YOB.Age.fctr.L Q109244.fctrYes:.clusterid.fctr3
## -0.0548137521 -0.4652264175
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.0442244695
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.2203118611 -0.0164878503
## Gender.fctrM Hhold.fctrMKy
## 0.1140742015 0.1034070965
## Hhold.fctrPKn Income.fctr.Q
## -0.3892296801 0.0597496040
## Income.fctr.C Q100689.fctrYes
## 0.0778545533 -0.0825903353
## Q101163.fctrDad Q101163.fctrMom
## 0.0919306457 -0.0620417500
## Q104996.fctrNo Q106042.fctrNo
## 0.0340483923 0.0303955769
## Q106389.fctrNo Q106997.fctrGr
## 0.0304847559 0.0833257766
## Q108855.fctrYes! Q109244.fctrNo
## 0.0338482572 0.3460957774
## Q109244.fctrYes Q110740.fctrMac
## -0.8383683060 -0.0335273621
## Q110740.fctrPC Q111220.fctrYes
## 0.0720542063 -0.0024957223
## Q112270.fctrNo Q112478.fctrNo
## -0.0004360146 0.0444971224
## Q113181.fctrNo Q113181.fctrYes
## -0.1195422670 0.1736312019
## Q113583.fctrTunes Q115195.fctrYes
## -0.0030507655 -0.0238639799
## Q115390.fctrNo Q115390.fctrYes
## 0.0163728293 -0.0546541871
## Q115611.fctrNo Q115611.fctrYes
## -0.1235163740 0.3391519759
## Q115899.fctrCs Q116881.fctrHappy
## -0.0595275431 -0.0237945727
## Q116881.fctrRight Q116953.fctrNo
## 0.1674378539 0.0415880568
## Q118232.fctrId Q118233.fctrNo
## -0.1056052655 0.0108836370
## Q119851.fctrNo Q120194.fctrStudy first
## 0.1095908511 -0.0502108640
## Q120379.fctrNo Q120379.fctrYes
## 0.0080943922 -0.0998188178
## Q120472.fctrScience Q120650.fctrYes
## 0.0911562463 0.0119719594
## Q121699.fctrYes Q122120.fctrYes
## -0.0381408439 0.0228220391
## Q122771.fctrPt Q124742.fctrNo
## 0.0914227249 -0.0183229077
## Q98197.fctrNo Q98197.fctrYes
## -0.2954726779 0.0169509648
## Q98869.fctrNo Q99480.fctrNo
## -0.1973799027 -0.0349090041
## Q99480.fctrYes YOB.Age.fctr.L
## 0.0602809159 -0.0688552257
## Q109244.fctrYes:.clusterid.fctr3 YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.4959597027 -0.0478237942
## [1] "myfit_mdl: train diagnostics complete: 28.406000 secs"
## Prediction
## Reference D R
## D 2115 836
## R 1123 1494
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.481681e-01 2.893849e-01 6.354600e-01 6.607208e-01 5.299928e-01
## AccuracyPValue McnemarPValue
## 2.243427e-71 1.035126e-10
## [1] "myfit_mdl: predict complete: 39.165000 secs"
## id
## 1 Final##rcv#glmnet
## feats
## 1 Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 26.949 2.384
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6437944 0.7167062 0.5708827 0.7076778
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.6040024 0.6343376
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.63546 0.6607208 0.2620574
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01204578 0.02430734
## [1] "myfit_mdl: exit: 39.181000 secs"rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 6 fit.data.training 3 0 0 206.511 246.29
## 7 fit.data.training 3 1 1 246.290 NA
## elapsed
## 6 39.779
## 7 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.55glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp
## Q109244.fctrYes 1.000000e+02
## Q109244.fctrYes:.clusterid.fctr3 2.726523e+01
## Hhold.fctrPKn 3.628540e+01
## Q109244.fctrNo 3.290204e+01
## Q115611.fctrYes 1.855961e+01
## Q98197.fctrNo 2.478156e+01
## Q98869.fctrNo 4.491932e+00
## Q113181.fctrYes 1.451086e+01
## Q116881.fctrRight 7.282315e+00
## Q115611.fctrNo 1.097807e+01
## Q113181.fctrNo 7.040219e+00
## Gender.fctrM 8.196442e+00
## Q119851.fctrNo 7.928697e+00
## Q118232.fctrId 1.506214e+00
## Hhold.fctrMKy 3.060185e+00
## Q120379.fctrYes 8.402531e+00
## Q101163.fctrDad 1.148138e+01
## Q120472.fctrScience 4.640923e+00
## Q122771.fctrPt 6.039696e+00
## Q106997.fctrGr 6.123942e+00
## Q100689.fctrYes 4.582868e+00
## Q110740.fctrPC 6.614133e+00
## Income.fctr.C 1.836203e+00
## YOB.Age.fctr.L 0.000000e+00
## Q101163.fctrMom 0.000000e+00
## Income.fctr.Q 4.137668e+00
## Q115899.fctrCs 7.231929e+00
## Q115390.fctrYes 0.000000e+00
## Q99480.fctrYes 1.225051e-01
## YOB.Age.fctr(35,40]:YOB.Age.dff 1.130848e+00
## Q120194.fctrStudy first 0.000000e+00
## Q112478.fctrNo 1.066220e+00
## Q99480.fctrNo 4.857361e+00
## Q116953.fctrNo 2.557984e+00
## Q121699.fctrYes 1.859613e-01
## Q108855.fctrYes! 2.465614e+00
## Q106042.fctrNo 1.043363e+00
## Q104996.fctrNo 1.009916e-01
## Q110740.fctrMac 0.000000e+00
## Q106389.fctrNo 7.962249e-02
## Q98197.fctrYes 1.515489e+00
## Q115195.fctrYes 2.326099e+00
## Q116881.fctrHappy 8.773345e-01
## Q122120.fctrYes 1.952439e-02
## Edn.fctr.L 0.000000e+00
## Q115390.fctrNo 0.000000e+00
## Q120379.fctrNo 0.000000e+00
## Q124742.fctrNo 2.611237e-01
## Q120650.fctrYes 3.912099e+00
## Q118233.fctrNo 0.000000e+00
## Q113583.fctrTunes 9.587133e-03
## Q111220.fctrYes 0.000000e+00
## Q112270.fctrNo 0.000000e+00
## .rnorm 0.000000e+00
## Edn.fctr.C 0.000000e+00
## Edn.fctr.Q 0.000000e+00
## Edn.fctr^4 0.000000e+00
## Edn.fctr^5 0.000000e+00
## Edn.fctr^6 0.000000e+00
## Edn.fctr^7 0.000000e+00
## Gender.fctrF 0.000000e+00
## Hhold.fctrMKn 0.000000e+00
## Hhold.fctrPKy 0.000000e+00
## Hhold.fctrSKn 0.000000e+00
## Hhold.fctrSKy 0.000000e+00
## Income.fctr.L 0.000000e+00
## Income.fctr^4 0.000000e+00
## Income.fctr^5 0.000000e+00
## Income.fctr^6 0.000000e+00
## Q100010.fctrNo 0.000000e+00
## Q100010.fctrYes 0.000000e+00
## Q100562.fctrNo 0.000000e+00
## Q100562.fctrYes 0.000000e+00
## Q100680.fctrNo 0.000000e+00
## Q100680.fctrYes 0.000000e+00
## Q100689.fctrNo 0.000000e+00
## Q101162.fctrOptimist 0.000000e+00
## Q101162.fctrPessimist 0.000000e+00
## Q101596.fctrNo 0.000000e+00
## Q101596.fctrYes 3.005049e+00
## Q102089.fctrOwn 0.000000e+00
## Q102089.fctrRent 1.386075e+00
## Q102289.fctrNo 0.000000e+00
## Q102289.fctrYes 0.000000e+00
## Q102674.fctrNo 0.000000e+00
## Q102674.fctrYes 0.000000e+00
## Q102687.fctrNo 0.000000e+00
## Q102687.fctrYes 0.000000e+00
## Q102906.fctrNo 0.000000e+00
## Q102906.fctrYes 0.000000e+00
## Q103293.fctrNo 0.000000e+00
## Q103293.fctrYes 0.000000e+00
## Q104996.fctrYes 0.000000e+00
## Q105655.fctrNo 0.000000e+00
## Q105655.fctrYes 0.000000e+00
## Q105840.fctrNo 0.000000e+00
## Q105840.fctrYes 0.000000e+00
## Q106042.fctrYes 0.000000e+00
## Q106272.fctrNo 0.000000e+00
## Q106272.fctrYes 8.207824e-01
## Q106388.fctrNo 0.000000e+00
## Q106388.fctrYes 2.196014e+00
## Q106389.fctrYes 0.000000e+00
## Q106993.fctrNo 0.000000e+00
## Q106993.fctrYes 0.000000e+00
## Q106997.fctrYy 0.000000e+00
## Q107491.fctrNo 0.000000e+00
## Q107491.fctrYes 0.000000e+00
## Q107869.fctrNo 0.000000e+00
## Q107869.fctrYes 0.000000e+00
## Q108342.fctrIn-person 0.000000e+00
## Q108342.fctrOnline 5.775531e-02
## Q108343.fctrNo 0.000000e+00
## Q108343.fctrYes 0.000000e+00
## Q108617.fctrNo 0.000000e+00
## Q108617.fctrYes 0.000000e+00
## Q108754.fctrNo 0.000000e+00
## Q108754.fctrYes 0.000000e+00
## Q108855.fctrUmm... 0.000000e+00
## Q108856.fctrSocialize 0.000000e+00
## Q108856.fctrSpace 0.000000e+00
## Q108950.fctrCautious 0.000000e+00
## Q108950.fctrRisk-friendly 0.000000e+00
## Q109244.fctrNA:.clusterid.fctr2 0.000000e+00
## Q109244.fctrNA:.clusterid.fctr3 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr2 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr3 0.000000e+00
## Q109244.fctrYes:.clusterid.fctr2 0.000000e+00
## Q109367.fctrNo 0.000000e+00
## Q109367.fctrYes 0.000000e+00
## Q111220.fctrNo 0.000000e+00
## Q111580.fctrDemanding 0.000000e+00
## Q111580.fctrSupportive 0.000000e+00
## Q111848.fctrNo 0.000000e+00
## Q111848.fctrYes 0.000000e+00
## Q112270.fctrYes 0.000000e+00
## Q112478.fctrYes 0.000000e+00
## Q112512.fctrNo 0.000000e+00
## Q112512.fctrYes 0.000000e+00
## Q113583.fctrTalk 0.000000e+00
## Q113584.fctrPeople 0.000000e+00
## Q113584.fctrTechnology 0.000000e+00
## Q113992.fctrNo 0.000000e+00
## Q113992.fctrYes 0.000000e+00
## Q114152.fctrNo 0.000000e+00
## Q114152.fctrYes 0.000000e+00
## Q114386.fctrMysterious 0.000000e+00
## Q114386.fctrTMI 0.000000e+00
## Q114517.fctrNo 0.000000e+00
## Q114517.fctrYes 0.000000e+00
## Q114748.fctrNo 0.000000e+00
## Q114748.fctrYes 0.000000e+00
## Q114961.fctrNo 0.000000e+00
## Q114961.fctrYes 0.000000e+00
## Q115195.fctrNo 0.000000e+00
## Q115602.fctrNo 0.000000e+00
## Q115602.fctrYes 0.000000e+00
## Q115610.fctrNo 0.000000e+00
## Q115610.fctrYes 0.000000e+00
## Q115777.fctrEnd 0.000000e+00
## Q115777.fctrStart 0.000000e+00
## Q115899.fctrMe 0.000000e+00
## Q116197.fctrA.M. 0.000000e+00
## Q116197.fctrP.M. 0.000000e+00
## Q116441.fctrNo 0.000000e+00
## Q116441.fctrYes 0.000000e+00
## Q116448.fctrNo 0.000000e+00
## Q116448.fctrYes 0.000000e+00
## Q116601.fctrNo 0.000000e+00
## Q116601.fctrYes 0.000000e+00
## Q116797.fctrNo 0.000000e+00
## Q116797.fctrYes 0.000000e+00
## Q116953.fctrYes 0.000000e+00
## Q117186.fctrCool headed 0.000000e+00
## Q117186.fctrHot headed 0.000000e+00
## Q117193.fctrOdd hours 0.000000e+00
## Q117193.fctrStandard hours 0.000000e+00
## Q118117.fctrNo 0.000000e+00
## Q118117.fctrYes 0.000000e+00
## Q118232.fctrPr 0.000000e+00
## Q118233.fctrYes 0.000000e+00
## Q118237.fctrNo 0.000000e+00
## Q118237.fctrYes 0.000000e+00
## Q118892.fctrNo 0.000000e+00
## Q118892.fctrYes 0.000000e+00
## Q119334.fctrNo 0.000000e+00
## Q119334.fctrYes 0.000000e+00
## Q119650.fctrGiving 0.000000e+00
## Q119650.fctrReceiving 0.000000e+00
## Q119851.fctrYes 7.669155e-02
## Q120012.fctrNo 0.000000e+00
## Q120012.fctrYes 0.000000e+00
## Q120014.fctrNo 0.000000e+00
## Q120014.fctrYes 0.000000e+00
## Q120194.fctrTry first 0.000000e+00
## Q120472.fctrArt 0.000000e+00
## Q120650.fctrNo 0.000000e+00
## Q120978.fctrNo 0.000000e+00
## Q120978.fctrYes 0.000000e+00
## Q121011.fctrNo 0.000000e+00
## Q121011.fctrYes 0.000000e+00
## Q121699.fctrNo 0.000000e+00
## Q121700.fctrNo 0.000000e+00
## Q121700.fctrYes 0.000000e+00
## Q122120.fctrNo 0.000000e+00
## Q122769.fctrNo 0.000000e+00
## Q122769.fctrYes 0.000000e+00
## Q122770.fctrNo 0.000000e+00
## Q122770.fctrYes 0.000000e+00
## Q122771.fctrPc 0.000000e+00
## Q123464.fctrNo 0.000000e+00
## Q123464.fctrYes 0.000000e+00
## Q123621.fctrNo 0.000000e+00
## Q123621.fctrYes 0.000000e+00
## Q124122.fctrNo 0.000000e+00
## Q124122.fctrYes 0.000000e+00
## Q124742.fctrYes 0.000000e+00
## Q96024.fctrNo 1.393278e+00
## Q96024.fctrYes 0.000000e+00
## Q98059.fctrOnly-child 0.000000e+00
## Q98059.fctrYes 0.000000e+00
## Q98078.fctrNo 0.000000e+00
## Q98078.fctrYes 0.000000e+00
## Q98578.fctrNo 0.000000e+00
## Q98578.fctrYes 0.000000e+00
## Q98869.fctrYes 0.000000e+00
## Q99581.fctrNo 0.000000e+00
## Q99581.fctrYes 0.000000e+00
## Q99716.fctrNo 0.000000e+00
## Q99716.fctrYes 0.000000e+00
## Q99982.fctrCheck! 0.000000e+00
## Q99982.fctrNope 0.000000e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr.C 0.000000e+00
## YOB.Age.fctr.Q 0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr^4 0.000000e+00
## YOB.Age.fctr^5 0.000000e+00
## YOB.Age.fctr^6 0.000000e+00
## YOB.Age.fctr^7 0.000000e+00
## YOB.Age.fctr^8 0.000000e+00
## Final..rcv.glmnet.imp imp
## Q109244.fctrYes 100.00000000 100.00000000
## Q109244.fctrYes:.clusterid.fctr3 56.54667024 56.54667024
## Hhold.fctrPKn 45.49136867 45.49136867
## Q109244.fctrNo 41.17704754 41.17704754
## Q115611.fctrYes 39.95609207 39.95609207
## Q98197.fctrNo 34.47809360 34.47809360
## Q98869.fctrNo 22.74079252 22.74079252
## Q113181.fctrYes 20.56297862 20.56297862
## Q116881.fctrRight 19.90540268 19.90540268
## Q115611.fctrNo 14.70827144 14.70827144
## Q113181.fctrNo 13.88552941 13.88552941
## Gender.fctrM 13.40694430 13.40694430
## Q119851.fctrNo 12.48597007 12.48597007
## Q118232.fctrId 11.70532049 11.70532049
## Hhold.fctrMKy 11.33551330 11.33551330
## Q120379.fctrYes 10.83163188 10.83163188
## Q101163.fctrDad 10.77381908 10.77381908
## Q120472.fctrScience 10.28783631 10.28783631
## Q122771.fctrPt 9.96153263 9.96153263
## Q106997.fctrGr 9.16423795 9.16423795
## Q100689.fctrYes 9.09618055 9.09618055
## Q110740.fctrPC 8.46775983 8.46775983
## Income.fctr.C 8.20820321 8.20820321
## YOB.Age.fctr.L 7.03368820 7.03368820
## Q101163.fctrMom 6.91827351 6.91827351
## Income.fctr.Q 6.52624527 6.52624527
## Q115899.fctrCs 6.39476574 6.39476574
## Q115390.fctrYes 6.36191954 6.36191954
## Q99480.fctrYes 6.27505992 6.27505992
## YOB.Age.fctr(35,40]:YOB.Age.dff 5.39948172 5.39948172
## Q120194.fctrStudy first 5.17146463 5.17146463
## Q112478.fctrNo 4.64091211 4.64091211
## Q99480.fctrNo 4.10694653 4.10694653
## Q116953.fctrNo 4.07819477 4.07819477
## Q121699.fctrYes 3.80792813 3.80792813
## Q108855.fctrYes! 3.41620474 3.41620474
## Q106042.fctrNo 3.39419807 3.39419807
## Q104996.fctrNo 3.35805904 3.35805904
## Q110740.fctrMac 3.34903501 3.34903501
## Q106389.fctrNo 2.90936294 2.90936294
## Q98197.fctrYes 2.22013742 2.22013742
## Q115195.fctrYes 2.17134566 2.17134566
## Q116881.fctrHappy 2.05767486 2.05767486
## Q122120.fctrYes 1.92298959 1.92298959
## Edn.fctr.L 1.73476461 1.73476461
## Q115390.fctrNo 1.15299231 1.15299231
## Q120379.fctrNo 1.14067446 1.14067446
## Q124742.fctrNo 1.11795542 1.11795542
## Q120650.fctrYes 0.48156417 0.48156417
## Q118233.fctrNo 0.42780274 0.42780274
## Q113583.fctrTunes 0.10869451 0.10869451
## Q111220.fctrYes 0.08891910 0.08891910
## Q112270.fctrNo 0.01553459 0.01553459
## .rnorm 0.00000000 0.00000000
## Edn.fctr.C 0.00000000 0.00000000
## Edn.fctr.Q 0.00000000 0.00000000
## Edn.fctr^4 0.00000000 0.00000000
## Edn.fctr^5 0.00000000 0.00000000
## Edn.fctr^6 0.00000000 0.00000000
## Edn.fctr^7 0.00000000 0.00000000
## Gender.fctrF 0.00000000 0.00000000
## Hhold.fctrMKn 0.00000000 0.00000000
## Hhold.fctrPKy 0.00000000 0.00000000
## Hhold.fctrSKn 0.00000000 0.00000000
## Hhold.fctrSKy 0.00000000 0.00000000
## Income.fctr.L 0.00000000 0.00000000
## Income.fctr^4 0.00000000 0.00000000
## Income.fctr^5 0.00000000 0.00000000
## Income.fctr^6 0.00000000 0.00000000
## Q100010.fctrNo 0.00000000 0.00000000
## Q100010.fctrYes 0.00000000 0.00000000
## Q100562.fctrNo 0.00000000 0.00000000
## Q100562.fctrYes 0.00000000 0.00000000
## Q100680.fctrNo 0.00000000 0.00000000
## Q100680.fctrYes 0.00000000 0.00000000
## Q100689.fctrNo 0.00000000 0.00000000
## Q101162.fctrOptimist 0.00000000 0.00000000
## Q101162.fctrPessimist 0.00000000 0.00000000
## Q101596.fctrNo 0.00000000 0.00000000
## Q101596.fctrYes 0.00000000 0.00000000
## Q102089.fctrOwn 0.00000000 0.00000000
## Q102089.fctrRent 0.00000000 0.00000000
## Q102289.fctrNo 0.00000000 0.00000000
## Q102289.fctrYes 0.00000000 0.00000000
## Q102674.fctrNo 0.00000000 0.00000000
## Q102674.fctrYes 0.00000000 0.00000000
## Q102687.fctrNo 0.00000000 0.00000000
## Q102687.fctrYes 0.00000000 0.00000000
## Q102906.fctrNo 0.00000000 0.00000000
## Q102906.fctrYes 0.00000000 0.00000000
## Q103293.fctrNo 0.00000000 0.00000000
## Q103293.fctrYes 0.00000000 0.00000000
## Q104996.fctrYes 0.00000000 0.00000000
## Q105655.fctrNo 0.00000000 0.00000000
## Q105655.fctrYes 0.00000000 0.00000000
## Q105840.fctrNo 0.00000000 0.00000000
## Q105840.fctrYes 0.00000000 0.00000000
## Q106042.fctrYes 0.00000000 0.00000000
## Q106272.fctrNo 0.00000000 0.00000000
## Q106272.fctrYes 0.00000000 0.00000000
## Q106388.fctrNo 0.00000000 0.00000000
## Q106388.fctrYes 0.00000000 0.00000000
## Q106389.fctrYes 0.00000000 0.00000000
## Q106993.fctrNo 0.00000000 0.00000000
## Q106993.fctrYes 0.00000000 0.00000000
## Q106997.fctrYy 0.00000000 0.00000000
## Q107491.fctrNo 0.00000000 0.00000000
## Q107491.fctrYes 0.00000000 0.00000000
## Q107869.fctrNo 0.00000000 0.00000000
## Q107869.fctrYes 0.00000000 0.00000000
## Q108342.fctrIn-person 0.00000000 0.00000000
## Q108342.fctrOnline 0.00000000 0.00000000
## Q108343.fctrNo 0.00000000 0.00000000
## Q108343.fctrYes 0.00000000 0.00000000
## Q108617.fctrNo 0.00000000 0.00000000
## Q108617.fctrYes 0.00000000 0.00000000
## Q108754.fctrNo 0.00000000 0.00000000
## Q108754.fctrYes 0.00000000 0.00000000
## Q108855.fctrUmm... 0.00000000 0.00000000
## Q108856.fctrSocialize 0.00000000 0.00000000
## Q108856.fctrSpace 0.00000000 0.00000000
## Q108950.fctrCautious 0.00000000 0.00000000
## Q108950.fctrRisk-friendly 0.00000000 0.00000000
## Q109244.fctrNA:.clusterid.fctr2 0.00000000 0.00000000
## Q109244.fctrNA:.clusterid.fctr3 0.00000000 0.00000000
## Q109244.fctrNo:.clusterid.fctr2 0.00000000 0.00000000
## Q109244.fctrNo:.clusterid.fctr3 0.00000000 0.00000000
## Q109244.fctrYes:.clusterid.fctr2 0.00000000 0.00000000
## Q109367.fctrNo 0.00000000 0.00000000
## Q109367.fctrYes 0.00000000 0.00000000
## Q111220.fctrNo 0.00000000 0.00000000
## Q111580.fctrDemanding 0.00000000 0.00000000
## Q111580.fctrSupportive 0.00000000 0.00000000
## Q111848.fctrNo 0.00000000 0.00000000
## Q111848.fctrYes 0.00000000 0.00000000
## Q112270.fctrYes 0.00000000 0.00000000
## Q112478.fctrYes 0.00000000 0.00000000
## Q112512.fctrNo 0.00000000 0.00000000
## Q112512.fctrYes 0.00000000 0.00000000
## Q113583.fctrTalk 0.00000000 0.00000000
## Q113584.fctrPeople 0.00000000 0.00000000
## Q113584.fctrTechnology 0.00000000 0.00000000
## Q113992.fctrNo 0.00000000 0.00000000
## Q113992.fctrYes 0.00000000 0.00000000
## Q114152.fctrNo 0.00000000 0.00000000
## Q114152.fctrYes 0.00000000 0.00000000
## Q114386.fctrMysterious 0.00000000 0.00000000
## Q114386.fctrTMI 0.00000000 0.00000000
## Q114517.fctrNo 0.00000000 0.00000000
## Q114517.fctrYes 0.00000000 0.00000000
## Q114748.fctrNo 0.00000000 0.00000000
## Q114748.fctrYes 0.00000000 0.00000000
## Q114961.fctrNo 0.00000000 0.00000000
## Q114961.fctrYes 0.00000000 0.00000000
## Q115195.fctrNo 0.00000000 0.00000000
## Q115602.fctrNo 0.00000000 0.00000000
## Q115602.fctrYes 0.00000000 0.00000000
## Q115610.fctrNo 0.00000000 0.00000000
## Q115610.fctrYes 0.00000000 0.00000000
## Q115777.fctrEnd 0.00000000 0.00000000
## Q115777.fctrStart 0.00000000 0.00000000
## Q115899.fctrMe 0.00000000 0.00000000
## Q116197.fctrA.M. 0.00000000 0.00000000
## Q116197.fctrP.M. 0.00000000 0.00000000
## Q116441.fctrNo 0.00000000 0.00000000
## Q116441.fctrYes 0.00000000 0.00000000
## Q116448.fctrNo 0.00000000 0.00000000
## Q116448.fctrYes 0.00000000 0.00000000
## Q116601.fctrNo 0.00000000 0.00000000
## Q116601.fctrYes 0.00000000 0.00000000
## Q116797.fctrNo 0.00000000 0.00000000
## Q116797.fctrYes 0.00000000 0.00000000
## Q116953.fctrYes 0.00000000 0.00000000
## Q117186.fctrCool headed 0.00000000 0.00000000
## Q117186.fctrHot headed 0.00000000 0.00000000
## Q117193.fctrOdd hours 0.00000000 0.00000000
## Q117193.fctrStandard hours 0.00000000 0.00000000
## Q118117.fctrNo 0.00000000 0.00000000
## Q118117.fctrYes 0.00000000 0.00000000
## Q118232.fctrPr 0.00000000 0.00000000
## Q118233.fctrYes 0.00000000 0.00000000
## Q118237.fctrNo 0.00000000 0.00000000
## Q118237.fctrYes 0.00000000 0.00000000
## Q118892.fctrNo 0.00000000 0.00000000
## Q118892.fctrYes 0.00000000 0.00000000
## Q119334.fctrNo 0.00000000 0.00000000
## Q119334.fctrYes 0.00000000 0.00000000
## Q119650.fctrGiving 0.00000000 0.00000000
## Q119650.fctrReceiving 0.00000000 0.00000000
## Q119851.fctrYes 0.00000000 0.00000000
## Q120012.fctrNo 0.00000000 0.00000000
## Q120012.fctrYes 0.00000000 0.00000000
## Q120014.fctrNo 0.00000000 0.00000000
## Q120014.fctrYes 0.00000000 0.00000000
## Q120194.fctrTry first 0.00000000 0.00000000
## Q120472.fctrArt 0.00000000 0.00000000
## Q120650.fctrNo 0.00000000 0.00000000
## Q120978.fctrNo 0.00000000 0.00000000
## Q120978.fctrYes 0.00000000 0.00000000
## Q121011.fctrNo 0.00000000 0.00000000
## Q121011.fctrYes 0.00000000 0.00000000
## Q121699.fctrNo 0.00000000 0.00000000
## Q121700.fctrNo 0.00000000 0.00000000
## Q121700.fctrYes 0.00000000 0.00000000
## Q122120.fctrNo 0.00000000 0.00000000
## Q122769.fctrNo 0.00000000 0.00000000
## Q122769.fctrYes 0.00000000 0.00000000
## Q122770.fctrNo 0.00000000 0.00000000
## Q122770.fctrYes 0.00000000 0.00000000
## Q122771.fctrPc 0.00000000 0.00000000
## Q123464.fctrNo 0.00000000 0.00000000
## Q123464.fctrYes 0.00000000 0.00000000
## Q123621.fctrNo 0.00000000 0.00000000
## Q123621.fctrYes 0.00000000 0.00000000
## Q124122.fctrNo 0.00000000 0.00000000
## Q124122.fctrYes 0.00000000 0.00000000
## Q124742.fctrYes 0.00000000 0.00000000
## Q96024.fctrNo 0.00000000 0.00000000
## Q96024.fctrYes 0.00000000 0.00000000
## Q98059.fctrOnly-child 0.00000000 0.00000000
## Q98059.fctrYes 0.00000000 0.00000000
## Q98078.fctrNo 0.00000000 0.00000000
## Q98078.fctrYes 0.00000000 0.00000000
## Q98578.fctrNo 0.00000000 0.00000000
## Q98578.fctrYes 0.00000000 0.00000000
## Q98869.fctrYes 0.00000000 0.00000000
## Q99581.fctrNo 0.00000000 0.00000000
## Q99581.fctrYes 0.00000000 0.00000000
## Q99716.fctrNo 0.00000000 0.00000000
## Q99716.fctrYes 0.00000000 0.00000000
## Q99982.fctrCheck! 0.00000000 0.00000000
## Q99982.fctrNope 0.00000000 0.00000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr.C 0.00000000 0.00000000
## YOB.Age.fctr.Q 0.00000000 0.00000000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr^4 0.00000000 0.00000000
## YOB.Age.fctr^5 0.00000000 0.00000000
## YOB.Age.fctr^6 0.00000000 0.00000000
## YOB.Age.fctr^7 0.00000000 0.00000000
## YOB.Age.fctr^8 0.00000000 0.00000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 109
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 3895 R NA
## 2 626 R 0.09865842
## 3 468 R NA
## 4 1515 R NA
## 5 1236 R NA
## 6 2749 R NA
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 <NA> NA
## 2 D TRUE
## 3 <NA> NA
## 4 <NA> NA
## 5 <NA> NA
## 6 <NA> NA
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 NA NA
## 2 0.9013416 FALSE
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1 0.1060872 D
## 2 0.1097240 D
## 3 0.1171322 D
## 4 0.1239887 D
## 5 0.1286311 D
## 6 0.1292165 D
## Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1 TRUE 0.8939128
## 2 TRUE 0.8902760
## 3 TRUE 0.8828678
## 4 TRUE 0.8760113
## 5 TRUE 0.8713689
## 6 TRUE 0.8707835
## Party.fctr.Final..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1 FALSE -0.4439128
## 2 FALSE -0.4402760
## 3 FALSE -0.4328678
## 4 FALSE -0.4260113
## 5 FALSE -0.4213689
## 6 FALSE -0.4207835
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 673 5908 R 0.4417477
## 882 4003 R NA
## 1202 6702 R NA
## 1431 4051 R 0.5093300
## 1658 696 D NA
## 1910 3625 D 0.6752376
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 673 D TRUE
## 882 <NA> NA
## 1202 <NA> NA
## 1431 D TRUE
## 1658 <NA> NA
## 1910 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 673 0.5582523
## 882 NA
## 1202 NA
## 1431 0.4906700
## 1658 NA
## 1910 0.6752376
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 673 FALSE 0.4586261
## 882 NA 0.4770211
## 1202 NA 0.5092640
## 1431 FALSE 0.5388377
## 1658 NA 0.5794423
## 1910 FALSE 0.6703034
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 673 D TRUE
## 882 D TRUE
## 1202 D TRUE
## 1431 D TRUE
## 1658 R TRUE
## 1910 R TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 673 0.5413739
## 882 0.5229789
## 1202 0.4907360
## 1431 0.4611623
## 1658 0.5794423
## 1910 0.6703034
## Party.fctr.Final..rcv.glmnet.is.acc
## 673 FALSE
## 882 FALSE
## 1202 FALSE
## 1431 FALSE
## 1658 FALSE
## 1910 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 673 FALSE
## 882 FALSE
## 1202 FALSE
## 1431 FALSE
## 1658 FALSE
## 1910 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 673 -0.09137392
## 882 -0.07297894
## 1202 -0.04073598
## 1431 -0.01116229
## 1658 0.02944228
## 1910 0.12030337
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2020 3433 D 0.7769289
## 2021 1393 D 0.7621309
## 2022 4956 D 0.7650150
## 2023 2641 D 0.7486126
## 2024 1311 D 0.7456295
## 2025 1309 D NA
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2020 R TRUE
## 2021 R TRUE
## 2022 R TRUE
## 2023 R TRUE
## 2024 R TRUE
## 2025 <NA> NA
## Party.fctr.All.X..rcv.glmnet.err.abs
## 2020 0.7769289
## 2021 0.7621309
## 2022 0.7650150
## 2023 0.7486126
## 2024 0.7456295
## 2025 NA
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2020 FALSE 0.7784465
## 2021 FALSE 0.7787471
## 2022 FALSE 0.7804032
## 2023 FALSE 0.7804716
## 2024 FALSE 0.7838502
## 2025 NA 0.8013847
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2020 R TRUE
## 2021 R TRUE
## 2022 R TRUE
## 2023 R TRUE
## 2024 R TRUE
## 2025 R TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 2020 0.7784465
## 2021 0.7787471
## 2022 0.7804032
## 2023 0.7804716
## 2024 0.7838502
## 2025 0.8013847
## Party.fctr.Final..rcv.glmnet.is.acc
## 2020 FALSE
## 2021 FALSE
## 2022 FALSE
## 2023 FALSE
## 2024 FALSE
## 2025 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 2020 FALSE
## 2021 FALSE
## 2022 FALSE
## 2023 FALSE
## 2024 FALSE
## 2025 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 2020 0.2284465
## 2021 0.2287471
## 2022 0.2304032
## 2023 0.2304716
## 2024 0.2338502
## 2025 0.2513847
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final..rcv.glmnet.prob"
## [2] "Party.fctr.Final..rcv.glmnet"
## [3] "Party.fctr.Final..rcv.glmnet.err"
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 7 fit.data.training 3 1 1 246.290 255.55
## 8 predict.data.new 4 0 0 255.551 NA
## elapsed
## 7 9.26
## 8 NAStep 4.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.55
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.55## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 109## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] 0.55## [1] "glbMdlSelId: All.X##rcv#glmnet"## [1] "glbMdlFinId: Final##rcv#glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## Low.cor.X##rcv#glmnet 0.5766816 0.5757966
## All.X##rcv#glmnet 0.5766816 0.5757966
## All.X##rcv#glm 0.5650224 0.5687249
## Interact.High.cor.Y##rcv#glmnet 0.5345291 0.5242069
## Random###myrandom_classfr 0.5300448 0.5181895
## Max.cor.Y.rcv.1X1###glmnet 0.5300448 0.5102459
## Max.cor.Y##rcv#rpart 0.5300448 0.5000646
## MFO###myMFO_classfr 0.5300448 0.5000000
## Final##rcv#glmnet NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## Low.cor.X##rcv#glmnet 0.5588180 0.6471308
## All.X##rcv#glmnet 0.5588180 0.6471308
## All.X##rcv#glm 0.5487933 0.6254219
## Interact.High.cor.Y##rcv#glmnet 0.5218093 0.6250526
## Random###myrandom_classfr 0.4836608 0.5299798
## Max.cor.Y.rcv.1X1###glmnet 0.4999322 0.6240737
## Max.cor.Y##rcv#rpart 0.4999322 0.6227308
## MFO###myMFO_classfr 0.5000000 0.5299798
## Final##rcv#glmnet NA 0.6343376
## opt.prob.threshold.fit
## Low.cor.X##rcv#glmnet 0.50
## All.X##rcv#glmnet 0.50
## All.X##rcv#glm 0.50
## Interact.High.cor.Y##rcv#glmnet 0.50
## Random###myrandom_classfr 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.45
## Max.cor.Y##rcv#rpart 0.50
## MFO###myMFO_classfr 0.50
## Final##rcv#glmnet 0.50
## opt.prob.threshold.OOB
## Low.cor.X##rcv#glmnet 0.55
## All.X##rcv#glmnet 0.55
## All.X##rcv#glm 0.65
## Interact.High.cor.Y##rcv#glmnet 0.70
## Random###myrandom_classfr 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.65
## Max.cor.Y##rcv#rpart 0.65
## MFO###myMFO_classfr 0.50
## Final##rcv#glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference D R
## D 440 151
## R 321 203## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## No 893.0164 241.6305 1134.7582 NA
## NA 840.5403 211.2200 1046.9636 NA
## Yes 182.7880 84.8165 296.3566 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## No 0.4403773 0.4466368 0.4468391 1961 249 373
## NA 0.3920952 0.3928251 0.3929598 1746 509 38
## Yes 0.1675275 0.1605381 0.1602011 746 223 NA
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## No 498 1038 1421 622 1961 622 2459 0.4852018
## NA 438 1171 1013 547 1746 547 2184 0.4822375
## Yes 179 742 183 223 746 223 925 0.4738352
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## No 0.4553883 NA 0.4614714
## NA 0.4814091 NA 0.4793790
## Yes 0.2450242 NA 0.3203855
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 1916.344770 537.667045 2478.078382 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 4453.000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## 981.000000 NA 1115.000000 2951.000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 2617.000000 1392.000000 4453.000000 1392.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 5568.000000 1.441275 1.181822 NA
## err.abs.trn.mean
## 1.261236## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp
## Q109244.fctrYes 100.000000
## Hhold.fctrPKn 36.285395
## Q109244.fctrNo 32.902038
## Q109244.fctrYes:.clusterid.fctr3 27.265227
## Q98197.fctrNo 24.781562
## Q115611.fctrYes 18.559608
## Q113181.fctrYes 14.510856
## Q101163.fctrDad 11.481378
## Q115611.fctrNo 10.978069
## Q120379.fctrYes 8.402531
## Gender.fctrM 8.196442
## Q119851.fctrNo 7.928697
## Q116881.fctrRight 7.282315
## Q113181.fctrNo 7.040219
## Q120472.fctrScience 4.640923
## Q98869.fctrNo 4.491932
## Hhold.fctrMKy 3.060185
## Q118232.fctrId 1.506214
## Final..rcv.glmnet.imp
## Q109244.fctrYes 100.00000
## Hhold.fctrPKn 45.49137
## Q109244.fctrNo 41.17705
## Q109244.fctrYes:.clusterid.fctr3 56.54667
## Q98197.fctrNo 34.47809
## Q115611.fctrYes 39.95609
## Q113181.fctrYes 20.56298
## Q101163.fctrDad 10.77382
## Q115611.fctrNo 14.70827
## Q120379.fctrYes 10.83163
## Gender.fctrM 13.40694
## Q119851.fctrNo 12.48597
## Q116881.fctrRight 19.90540
## Q113181.fctrNo 13.88553
## Q120472.fctrScience 10.28784
## Q98869.fctrNo 22.74079
## Hhold.fctrMKy 11.33551
## Q118232.fctrId 11.70532## [1] "glbObsNew prediction stats:"##
## D R
## 981 411## label step_major step_minor label_minor bgn end
## 8 predict.data.new 4 0 0 255.551 270.597
## 9 display.session.info 5 0 0 270.597 NA
## elapsed
## 8 15.046
## 9 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn end
## 2 fit.models 2 0 0 13.633 108.993
## 3 fit.models 2 1 1 108.993 188.699
## 6 fit.data.training 3 0 0 206.511 246.290
## 8 predict.data.new 4 0 0 255.551 270.597
## 4 fit.models 2 2 2 188.699 201.647
## 7 fit.data.training 3 1 1 246.290 255.550
## 1 select.features 1 0 0 5.859 13.632
## 5 fit.models 2 3 3 201.648 206.510
## elapsed duration
## 2 95.360 95.360
## 3 79.706 79.706
## 6 39.779 39.779
## 8 15.046 15.046
## 4 12.948 12.948
## 7 9.260 9.260
## 1 7.773 7.773
## 5 4.863 4.862
## [1] "Total Elapsed Time: 270.597 secs"
## label step_major step_minor label_minor
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 2 fit.models_0_MFO 1 1 myMFO_classfr
## 1 fit.models_0_bgn 1 0 setup
## bgn end elapsed duration
## 6 68.141 108.978 40.838 40.837
## 4 30.972 51.175 20.203 20.203
## 5 51.176 68.140 16.964 16.964
## 3 21.970 30.972 9.002 9.002
## 2 14.082 21.969 7.887 7.887
## 1 14.058 14.081 0.023 0.023
## [1] "Total Elapsed Time: 108.978 secs"